KONA Powder and Particle Journal No. 41 (2024) 3–25/https://doi.org/10.14356/kona.2024011

Review Paper

Artificial Intelligence and Evolutionary Approaches
in Particle Technology †Christoph Thon *, Marvin Röhl, Somayeh Hosseinhashemi, Arno Kwade and Carsten Schilde
Institute for Particle Technology (iPAT), Technische Universität Braunschweig, Germany

Since the early 2010s, after decades of premature excitement anddisillusionment, the field of artificial intelligence (AI) is experiencing
exponential growth, with massive real-world applications and highadoption rates both in daily life and in industry. In particle technology,
Whitebox
Digital twin
there are already many examples of successful AI applications,
Known models
Predictive AI
Process control
Process
for predictive modeling, process control and optimization, fault
Genetic programming
optimization
recognition, even for mechanistic modeling. However, in comparison
Reinforcement
Genetic
Active learningalgorithms
to its still untapped potential and to other industries, further expansionin adoption rates and, consequently, gains in productivity, efficiency,
and cost reduction are still possible. This review article is intended to
Simulation/ Physical process/ Data bankintroduce AI and its application scenarios and provide an overview andexamples of current use cases of different aspects and unit operations
in particle technology, such as grinding, extrusion, synthesis, characterization, or scale up. In addition, hybrid modeling approaches arepresented with examples of the intelligent combination of different methods to reduce data requirements and achieve beneficial synergies.
Finally, an outlook for future opportunities is given, depicting promising approaches, currently being in the conception or implementation
phase.
Keywords: artificial intelligence, genetic algorithms, predictive modeling, hybrid modeling, particle technology

1. Introduction

Artificial intelligence (AI) has reached critical applicability rates in recent years as a consequence of algorithmic
improvements, exponential gains in hardware and data
generation, and increasing improvements in available
infrastructure, such as the current focus on parallelizationand dedicated hardware solutions (ASICs) (LeCun et al.,2015).
Consequently, AI is increasingly being adopted in all
aspects of daily life and throughout industries. In particle
technology, there are many areas in which AI techniques
are already being applied, but dependent on the specificbackground such as the ability to rely on or generate large
amounts of data, security aspects, availability of sensors,
the ability to dynamically interact with processes, etc.,
which lead to varying rates of and requirements for the
adoption of AI. Practical examples encompass a wide variety of areas within particle technology, such as predictivemodeling of grinding results, kinetics, pathways, stability,
mixing and quality, preprocessing of data, shape control inparticle synthesis and parameter control in general, yield
optimization, separation via machine vision, use as soft†*

Received 18 May 2022; Accepted 6 September 2022
J-STAGE Advance published online 15 July 2023
Corresponding author: Christoph Thon;
Add: Volkmaroder Str. 5, D-38104 Braunschweig, Germany
E-mail: c.thon@tu-braunschweig.de
TEL: +49-531-391-65553 FAX: +49-531-391-9633

sensors, improvement of measurement results, surrogate
modeling, etc.
Similarly, AI itself is an umbrella term. This review article builds on more general articles about AI in the context
of process engineering (Thon et al., 2021; Pham and Pham,
1999; Linkens, 1990; Mavrovouniotis, 2012; Lee J.H. etal., 2018) and focuses on the context of particle technology
specifically. Furthermore, general reviews exist, such asof mechanical engineering (Patel et al., 2021), electricalengineering (Sakunthala et al., 2017), material science(Pilania, 2021), and chemical engineering (Schweidtmann
et al., 2021; Ashraf et al., 2021; Venkatasubramanian,2019). On the other hand, focused review articles for
specific subfields within particle technology exist, such as
for related autonomous processes (Nirschl et al., 2022) or
nanoparticle drug formulations (Uhlemann et al., 2021),
although, to date, no conclusive overview of the field ofparticle technology has been provided, which is the focusof this review article.
In this paper, prominent techniques such as neural nets
or evolutionary algorithms are discussed and an overview
of hybrid modeling techniques and examples of ensemble
methods (the intelligent combination of techniques) areprovided.
In the following, based on a short summary of important
AI methods, examples of applying AI to important aspectsof particle technology, including unit operations such as
grinding, synthesis, modification, or classification, as well

Copyright © 2024 The Authors. Published by Hosokawa Powder Technology Foundation. This is an open accessarticle under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

3

Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

as particle analysis and scale up procedures, are reviewed.
Finally, an overview is provided to depict near-term opportunities and untapped potentials.

2. Introduction into Artificial Intelligence

AI encompasses a wide spectrum of techniques likeartificial neural networks (ANN), genetic algorithms (GA),
Hidden Markov Models, and others. First introduced at the
famous 1956 Dartmouth conference with the goal to emulate general human cognitive capabilities, AI experienced
three major waves of enthusiasm and disillusionment asa consequence of initial hype and overestimations on one
side and hardware and software limitations on the other.
It was not until the early 2010s that AI experienced major
breakthroughs in real-world applications and adoption
rates, with the most common AI techniques used today
being deep neural networks (DNNs) (Ongsulee, 2017).
Fig. 1 depicts the basic principle of ANNs. Being
loosely inspired by the brain, the fundamental building
blocks are neurons, which are connected. The neurons arearranged in layers, with the first layer being denoted as the
input layer and the last one as the output layer. Each input
and output node is a representation of an input and output,
e.g., a parameter of a process, a pixel in an image, or aword in a text. Inputs can be binary, integers, or continuous
values. Between these two layers are a number of hidden
layers, in which the modeling is taking place. Between theneurons of neighboring layers, the different connections
have dedicated weights associated with them.
As can be seen in the right part of Fig. 1, which iszoomed out for one representative neuron, inputs are
multiplied with their respective weights and consequently
summed. The summed weight is further processed via an
activation function; the resulting value is then propagatedforward to successive neurons of the following layer.
Through different activation functions, linearities, non-

Review Paper

linearities, or other patterns can be introduced, with theresulting behaviors of the overall system changing, suchas spiking neural nets appearing, etc. Being a hierarchicalstatistical system, ANNs have generalizing capabilities
because they process more complex patterns of a higher
order on the basis of detected lower-order patterns ofmore limited complexity. The goal of AI training is theiterative optimization of connection weights in order to
get correct outputs for given inputs based on training data.
The classical approach for this is backpropagation in which
after each iteration, a loss function is determined for the
successive analytical approximation and adjustment of theweights. Specifically, for large AI systems with an abundance of training data, backpropagation is the classicallyused training approach, although it has a strong tendency
toward overfitting in cases with limited data sets. Alternative systems exist, which are focused on training withmore limited data, such as genetic reinforcement learning(GRL), which was described in greater detail in Thon etal. (2022b) and can be described as the training of AI via a
genetic algorithm.
In addition, there exists a plethora of techniques toprevent overfitting, which can also be combined to achieve
more general models, such as regularization, feature
selection, early stopping, layer removal, or dropout. Regularization encompasses techniques to prevent the trainingof too complex models, e.g., through the use of penalties orchanges in the neural network architecture upon training.
L1 (Lasse regularization) and L2 (ridge regression) modify
the penalties in training by adding an absolute value or thesquared value of magnitude (Demir-Kavuk et al., 2011).
In dropout, as another regularization method, neurons are
randomly deactivated during training to prevent excessive
co-adaptation (Srivastava et al., 2014). Feature selection
is another method to prevent overfitting by restricting
the training to only the most relevant features in case of

y
x

0

0

2

2

1

∑

Hiddenlayer I

Hiddenlayer II

Output
layer

Input

…

…

Input
layer

1

Weightyyxx

Activation
function∑()

Summation()
Treshold

OutputFig. 1 Depiction of a neural net, with an input, an output, and two hidden layers. Connections between neurons of layers feature dedicated weights.
Incoming inputs from former layer neurons are summed and multiplied according to an activation function, e.g., to introduce non-linearities.

4Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

the initial number of features in the data being too large(Hawkins, 2004). Early stopping describes the techniquesof evaluating training over epochs with independent data(also termed “hold up”) to quit training once a degradationof the loss becomes detectable, indicating overfitting anda loss in generalizing capabilities (Ying, 2019). Crossvalidation, as a more advanced version of hold up, allows
for training and evaluation with independent data, withvarying assignments of the data groups to train and test
data, allowing all data to be used for training (Schaffer,1993).
GAs are another tool often used, specifically for optimization tasks but also for modeling and regulatory
applications. As shown on the left in Fig. 2, GAs are a
population-based approach that mimics Darwinian evolution in biology.
As a starting point, a population of parameter sets (usually consisting of a fixed and, throughout the population,
identical number of entries with variable values) is evaluated according to a stated goal. If, for instance, processparameters in a stirred media mill are to be optimized withregard to maximum energy efficiency, production yield,or the optimal combination of both KPI, measures for the
optimization goal or for multiple goals are established, andthe optimizable parameters are defined. Evaluation of thespecified goals of initial parameter sets is performed for theselection of the most suitable parameter sets. A subsequentcrossover of selected areas in the parameter space results in
a new generation with mixed attributes from the previous
generation. In addition, mutations are induced at specified
rates to increase the “genetic” diversity of the populationand to avoid optimization within local minima. Throughiterative execution over multiple generations, eventually(under constant environmental conditions), a steady stateis reached with ideally close to optimal parameter settings.
Genetic programming, shown on the right side of Fig. 2,
applies the same steps for the iterative evolution of decision tree structures or decision trees. These tree structures
can represent computer programs, with respective operators and numbers being the nodes in the tree, to automatically develop code. Alternatively, the trees can represent
mathematical operators for the evolution of fitting equation
systems for the description of data sets for modeling tasks.
The technical term for genetic programming, when applied
to formulas, is symbolic regression. In principle, all treebased tasks can be optimized with genetic programming.
A more detailed summary of underlying AI techniques
can be found in more general review papers (Krishna etal., 2018; Rupali and Amit, 2017; Raschka et al., 2020;
Rahmani et al., 2021; Thon et al., 2021).
Key criticisms of AI are usually its inherent lack oftransparency and its empirical nature. Different methods
exist to address these issues including explainable AI(XAI), reverse engineering strategies, and hybrid or graybox modeling. Often, respective disadvantages of different
AI methods can be compensated for by the intelligent combination of different methods such as ensemble methods
or hybrid approaches (Stosch et al., 2014). For instance,
as seen in Fig. 3, predictive neural net modeling as a blackbox can be utilized in order to create a model with generalizing capabilities for the subsequent prediction of virtual
training data for genetic programming.
The generalizing capabilities of genetic programmingare more limited than those of neural nets. The techniqueis, however, intrinsically more transparent. In conjunction
with additional methods, genetic programming even allows

Genetic Algorithmd) Mutation ofindividuals

Genetic Programminga) New
population^

2

Mutatione) Cycle ofgenerations

0
3

1
0

2
3

+
5

^
y

+
sin

y

b) Evaluationand selection

3

7

y

x

x

^
y

x

3

x
√

sin

/
√

+

√

Crossover

+

sin

5

/
x

x

+
c) Reproduction
and crossover

Review Paper

√
x

/
3

√

x

*
x

1

Combination of formula components

Fig. 2 Two common evolutionary algorithms, genetic algorithms and genetic programming, emulating Darwinian evolution on technical use cases.
On the left, settings for a fixed set of parameters are optimized via successive generations of parameter mutations, crossover between candidates, andthe evaluation and selection of suitable candidates in the population. On the right, the same principles are applied in genetic programming, but on thebasis of decision trees of theoretically unconstrained complexity, representing computer code, rule sets, or mathematical equations, with the latter
one called symbolic regression. Reprinted from Ref. (Thon et al., 2022b) under the terms of the CC-BY 4.0 license. Copyright: (2022) The Authors,published by Elsevier.

5

Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

Semiparametric model
Parallel model

Serial model

b

White
box
Model
c=f(b)

Combinatorial modelcaaModular model

Review Paperc
White boxModelc=f(b)

Fig. 3 A combination of predictive models based on neural nets to gain generalizing capabilities and genetic programming for the subsequent
deduction of more transparent mathematical models, in which equation modeling is performed on the basis of predictions from the neural net model.
Examples are provided for the results of both approaches, with blue columns marking training data and orange lines the respective model prediction.
Furthermore, hybrid modeling approaches are depicted, as well as serial and parallel semi-parametric models, modular models, and a possible combinatorial approach.

for the incorporation of physical relations (Udrescu et al.,2020).
In addition, as can be seen in Fig. 3, for an exemplary
data foundation (blue), the generated equation represents
a simpler (more transparent), though still accurate, relation
when compared to neural net predictions (orange lines).
Similarly, McKay et al. (1996) proposed a nonlinear
dynamic model using two different paradigms. In thiswork, they tried to compare the recurrent neural networkand the genetic programming algorithm to build a viscosity
model. They showed that in this particular case, the genetic
programming model has simpler transparency than neuralnetworks.
Beyond the combination of neural nets and evolutionary
approaches, hybrid models, shown in the lower part of
Fig. 3, are well suited for mechanistic modeling and theincorporation of existing empirical or white-box models.
In a serial approach, a white-box model can be applied topreprocessed data from AI or in a direction opposite to thepreprocessed data based on known relations for subsequent
AI modeling. Running in parallel, black-box AI models

6

can be used to compensate for the shortcomings of existing
white-box models by merely modeling the divergence
between white-box model predictions and reality. The parallel AI can, for instance, be broken down again via genetic
programming to iteratively extend the white-box model(Thon et al., 2021, 2022b; Evans et al., 2019). In modular
hybrid models, an interconnected system of sub-AI models
is set up to mirror the known structure of its physical counterpart and facilitate the modeling effort. Combinations ofhybrid models are possible, such as a mixture of a serial
and a parallel semiparametric model, as Psichigios andUngar (1992) demonstrated.
In addition, a wide variety of reverse engineering strategies exist to deconstruct or deduce the inner workings oftrained AI systems, such as sensitivity analysis, dropout,deconvolution, rationalization, LIME, GAM, gradientdescent, neuron activation, etc.

3. Applications of AI in particle technology

Table 1 provides an overview of AI applications forvarious categories of particle technology unit processes.

Characterization/
analysis

Operations

Size estimation in grinding (ball mill, spiral classifier)

Dai et al.

Rheology

Online soft sensor: Particle size estimation for hydrocyclone
overflow, combined with ball mill and classifier

Tie et al.

Combined
System

Fit rheology model via a genetic algorithm
Electron transfer properties (e.g., Femi energy) dependent on the
structural and morphological features of silver nanoparticles

Finke et al.

Sun et al.

Size estimation in classification via a classifier (ball mill, spiralclassifier)Hoseinian
et al.
Dai et al.

Classification/
RemovalData rectification through image recognition of a physical stirred
reactor coupled with a CFD simulation to act as the surrogate
model for process control
Prediction of chemical equilibrium states effecting precipitation
Prediction of process kinetics for flocculation, crystallization, andfracture to model the kinetics of nucleation, growth, shrinkage,
fracture, and agglomeration
Prediction of ion flotation-based Zn(II) removal

Khaydarov
et al.

Leal et al.
Nielsen etal.

Process control of fat addition for feed pellets for animals

Ittiphalin
et al.

Zhang et al.

Li et al.
Chen et al.

Pellegrino
et al.

Process parameter-dependent prediction of TiO2 nanoparticle
morphology, size, polydispersity, and aspect ratio in hydrothermal
synthesis. Reverse engineering for optimal process conditions
Identify likely synthesis patterns of gold nanoclusters
Prediction and classification of energy expressions on the basis of
structure and energy in ZnO nanoparticle morphologies
Control particle distribution in a cobalt oxalate synthesis process

Predictive modeling of kinetic energies in a wet stirred media millwith subsequent mechanistic modeling via genetic programming
Particle size estimation in grinding (ball mill, spiral classifier)Thon et al.

Dai et al.

Description
Fault recognition in the grinding process

ExampleQu et al.

Aggregation

MixingSynthesis

Grinding

Mechanistic modeling; GA
Predictive modeling

Predictive modeling

Predictive modelingand classification

Preprocessing anddata rectification forprocess control
Predictive modeling
Hybrid based on
preexisting process
knowledge
Hybrid ANN-GA;
predictive modeling
Predictive modeling

Process optimization
Classification andpredictive modeling
Hybrid model;
process control
Predictive modelingfor process control

Modeling and processoptimization

Predictive and mechanistic modeling
Predictive modeling

Task (process and AI)
Classification
Simulation

Data foundation
Experiments

ANN (three layers)

Hybrid; ANN: random
vector functional-link
network
Modular-hybrid: 2x
ANN; radial basis
function networks
Hybrid; ANN: random
vector functional-link
networkGA

ANN-GANA
NA

DNN (YOLO model);serial hybrid model

LLM, BPNN

NA

NA
NA

Experiments

Experiments

Experiments

NA

Experiments

NA

Preexisting database of casestudy
Mixed experimental andsimulation setup
Simulations
Experimental(sensor data)

NA

NA
NA

Hybrid; ANN: random NA
vector functional-link
networkNANA

AI type
ANNs: BP, LSTM,
Auto-Encoder
ANN, GA, GP

Table 1 Overview (state July 2022) of some examples for AI in particle technology with the author, a broad description, the task, and used AI types, as well as the data foundation, sorted according to a classification
scheme featuring unit operations, and tasks. Abbreviations are defined in the nomenclature before the references.

Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25
Review Paper

7

Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

The four classical unit operations of grinding, separation/
classification, mixing, and agglomeration/granulation are
elaborated on later in this paper, as well as bottom-up
methods such as particle synthesis and characterization
methods, including rheology.
Unit operations in particle technology are sometimes
applied in isolation. In industrial operations, however,they are often combined and interconnected in complex
plants. Possible assemblies vary to a significant degree,with some being more prominent. As can be seen in
Fig. 4, the characterization of educts, products, or intermediary products can accompany the entire production
flow and is often performed online during processes,
as is modeling and the scale-up of operational steps.
Mixing is an important operation embedded in the beginning of a process, repeated throughout the process
flow, or integrated into other unit operations. Grinding
as a top-down approach and synthesis as a bottomup approach are typical operations for the production ofparticles. Stabilization/functionalization, agglomeration, or
precipitation are unit operations often performed successively after the production of primary particles, although
integrative approaches such as parallel stabilization during
grinding or synthesis are also often applied. After production, primary particles, agglomerates, or aggregates can
be classified, e.g., according to their size, morphology, orother characteristics and can be further processed, e.g., via
extrusion. This brief overview is intended as a simplified
representation of inter-dependencies and how unit operations can be integrated and connected in practical applications. The individual unit operations and accompanying
steps are discussed in regard to the application of AI andevolutionary approaches.
In this regard, descriptions are given together with the
AI-specific tasks (predictive modeling, classification, optimization, etc.) and the used types of AI (ANNs, genetic,
algorithms, and hybrid models, etc.), as well as the training
data source (experiments, simulations, data banks, etc.).
In the following, the respective examples for the different
categories are described in more detail.
At the time of this review article, numerous works existed. However, distribution throughout the different tasks

Review Paperand unit operations is inhomogeneous, with many mostly
small-scale academic applications and a much smaller
number of practical industrial adoption cases for large
connected systems or plants. Distribution between individual unit operations and accompanying steps is similarly
inhomogeneous, with most publications focusing on initial
unit operations such as grinding, while examples for later
unit operations such as extrusion are rarer. Furthermore,most of the published works with real-world applicability
are very recent since they became possible only in the last
five years, with more publications appearing closer to thedate of this review. This indicates that there is significant
untapped potential in the field for the adoption of AI techniques and aligns well with adoption rates in most otherindustries, with some featuring more advanced adoption
rates.

3.1 Grinding

A plethora of techniques for the creation of productparticles exists, as is elaborated on in subsequent chapters.
Grinding is the most widespread technique used throughout many industries for a large variety of materials andproducts. As a top-down approach, it is suitable to costeffectively process large quantities of material. With a developmental history almost as old as human cultural history
itself, starting with the grinding of grains, modern available
equipment is well established, sophisticated, and available
in a large variety for more specialized applications such asmechanochemical reactions.
AI-based preprocessing can, for instance, be applied
as an initial step for predictive modeling. Going to moresophisticated systems, modular predictive models allow for
the modeling of more complex plants, which can be aided
by hybrid modeling and white-box model combinations.
Based on this optimization, anomaly detection and mechanistic modeling can be utilized, and examples for theseincreasingly sophisticated models are provided.
Dimensionality reduction: As depicted in Fig. 5, oneof the first possible tasks that can be performed in an
AI process flows is data preprocessing and dimensionality reduction, for instance, through the use of auto
encoders. With grinding, Qu et al. (2017) trained a hybrid

Characterizations

EductsMixingSynthesis
Grinding

Stabilization
Agglomeration
Precipitation

Classification
Extrusion

Modeling and Scale-up

Fig. 48

Unit operations in particle technology.

Products

Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

auto-encoder-Softmax network and an RNN-LSTM network for fault recognition in a grinding system. One thousand experimentally gained data points were used, with 30
dimensional input features.
Via the reduction of the number of involved dimensions,the complexity of the data set can be drastically reduced,
making successive conventional or AI modeling easier. In
Qu et al., dimensionality can be reduced from 30 dimensions (such as temperatures, pressures, sounds, vibrations
in different regions of the grinding system) to 24 (Qu et al.,2017). Input and output layers are set up identically. The
encoder side is intended to reverse engineer and reconstruct
the previously decoded representations from the input side
as accurately as possible. The narrowing of intermediaryhidden layers enforces more compact representations with
reduced features.
Predictive modeling of single units: Based on experimental data, the authors of this review article trained pre-Output layer
Decoder
Hidden-Intermediate
representation
Encoder
Input layer

Fig. 5 Autoencoder networks with an encoder and a decoder side areoften used for dimensionality reduction, as smaller hidden layers act
as a bottleneck, forcing intermediary representations with narrower
features.

Review Paper

dictive models (see Fig. 6), for grinding Quarz in a stirred
media mill (PM1 from Bühler). Input process parameterssuch as tip speed, solid content, and fluid volume flow werevaried, and additional values for torque and power were
measured. An ANN was then optimized and trained.
The cumulative particle size distributions (PSD) werenormalized and represented via a discretized grid, withthe distribution curve being in a grid cell translating toa cell value of 1 or 0. The resulting binary values wereassigned to the output nodes of the net. Consequently,
for novel input parameter settings, heat-maps can begenerated to mark the likelihood that the respective
cumulative curve will pass through a cell. During postprocessing, resulting scatter plots were derived and converted to approximation cumulative curves. In the rightlower part of Fig. 6, predicted X50 (describing the particle
size within the distribution, at which 50 percent of particles
are smaller and 50 percent larger) values are depicted over
the respective X50 values of the independent evaluation data
points. The approach was further optimized for the prediction of fitting parameters, which is elaborated on later in thecontext of describing a predictive breakage tester AI.
Modular predictive hybrid modeling of plants: Dai etal. proposed a parallel semiparametric hybrid particle size
estimation model, in combination with a random vector
functional-link network (RVLFN), with the examined
grinding circuit mainly consisting of an interconnected system comprising a ball mill and spiral classifier (see Fig. 7).
The ball mill’s first principle model was based on population balance equations; the classifier was described by its
own mechanistic model (Dai et al., 2015).
Based on these these underlying models, sensitivity

PSD…

Inputs x:
TorqueTip speed t
Solid content
Volume �lowTime
Net power

Outputs y:[0, 0, 1, . . . ]Fig. 6 Predictive model for experimental grinding results. For varied process parameters such as tip speed and solid content, resulting particle sizes
were predicted, with one output for X50 (marking the particle size in the distribution, where 50 percent of the particles are smaller and 50 percent arelarger) and the other a heat map for the representation of the entire particle size distribution (original work by author).

9Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

analysis and pre-predator optimization algorithms were
utilized in order to reduce parameter optimization complexity. For the resulting error, the RVFLN was trained tocompensate for the first principle method’s error, resulting
in an overall system with significantly higher accuracy (Daiet al., 2015).
Similarly, Tie et al. (2005) demonstrated PSD estimation via an on-line soft sensor for a setup consisting of ahydrocyclone overflow and a multitude of accompanying
machines such as ball mills in two connected semi-circular
grinding circuits, as shown in Fig. 8.
The underlying basic structures were divided utilizing a
modular approach, while the classifier and hydrocyclone
were described with respective ANNs.
The percentages of solids in the mass flow with diameters smaller than 75 μm, M75, were modeled via AI in dependence of masses, concentrations, and respective flows
for both the cyclone and spiral classifier. Mass percentage
was furthermore modeled in dependence of multiple relevant parameters such as shaft power and recycle slurryflow rate. Sump modeling was based on a first principle
population balance equation approach. The respective nets
acting as functions in the overall setup (f1 for the cyclone
and f2 for the classifier) were both radial basis function
networks (RBF). In addition to the described models for themodeling of nominal process conditions, an adaptive neuro

Fig. 7 Milling setup consisting of a bin, a feeder, the ball mill, and aspiral classifier. Reprinted with permission from: Ref. (Dai et al., 2015).
Copyright: 2015 B.V.

Review Paper

fuzzy inference system (ANFIS) model was trained for the
upstream prediction of anomalies and malfunction, e.g., incase of excessive slurries. A subsequent fuzzy logic coordinator was applied for model connection (Tie et al., 2005).
Simulation-based modeling and breakdown formechanistic modeling: AI modeling based on simulations
is an approach with distinct advantages due to the couplingof two numerical approaches, as they can autonomouslyinteract with one another unsupervised (for instance in
active learning (AL), etc.). As an example, on the basis of
two-way coupled computer fluid dynamics - discrete element method (CFD-DEM) simulations with Rocky DEM
and Ansys Fluent, a representative slice of a wet operated
stirred media mill was simulated with varying tip speeds
and grinding bead diameters (Thon et al., 2022a; 2022b).
Heatmaps were extracted, with each voxel representing the
averaged values of relative velocities. Fig. 9 depicts theresults of the approach.
On the left side of Fig. 9, heatmaps for an independent
test case not used in training are shown, with the prediction
respectively depicted over the independent simulation
result. Averaged values in 14 layers, as well as the density
distribution of the entire domain for relative velocity,
demonstrate the good fit of the result. The results also
proved the advantages of GRL, as the models achieved significantly more accurate results in comparison to classicalbackpropagation. Finally, genetic programming was applied to identify a fitting equation to describe the relationship between the relative velocities 99 of colliding beads(99 percent had smaller energies) and the absolute tip speedof the stirrer. Depicted in the lower right corner of Fig. 9,
parameter settings in random order are depicted, with redmarking the equation and black the evaluation data.
The methodology was further improved and adapted to avertical dry stirred media mill (original unpublished workby the author), demonstrating its universality, regardless of
wet and dry states in grinding. In this case, a vertical mill
was numerically investigated in Rocky DEM. Restitution
coefficients and friction coefficients were determined andcalibrated beforehand. Stirrer tip speed, grinding bead size,and grinding aids for the variation of powder flowability

Fig. 8 Investigated connected setup with multiple machines used for PSD prediction with machine learning in a soft-sensor approach. Reprinted withpermission from Ref. (Tie et al., 2005). Copyright: 2005 B.V.

10

Translational velocity mean [m/s]Particle number distribution/ Histogram [-]Pre_Kinetic_Distribution
Test_Kinetic_Distribution
Histogramm / predicted
Histogramm / Test

Cumulative distribution function [-]Kernel Density Estimation [-]Line distance [-]AI prediction

AI prediction
Simulation

Simulation

Review Paper

Line distance [-]Simulation

AI prediction

Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

AI prediction
Simulation

Layers [-]Translational velocity mean [m/s]Fig. 9 A predictive mill model trained on the basis of a coupled CFD-DEM simulation. The simulation and prediction domain were discretized forthe predictive modeling of the relative velocities of colliding beads as a measure of kinetic energy via heatmaps. Comparisons between AI predictions
for independent simulation cases, not used for training, are depicted, showing good accuracy. Furthermore, a comparison of averaged values for characteristic regions in 14 layers of the mill, as well as for the entire domain, is provided. Finally, with genetic programming, a fitting equation describing
the relationship of relative velocity 99 with tip speed was identified, being depicted for 22 parameter combinations in random order. Adapted from
Ref. (Thon et al., 2022b) under the terms of the CC-BY 4.0 license. Copyright: (2022) The Authors, published by Elsevier.

(considered via the preliminary determined calibration
parameters in the simulation) were chosen as input parameters, and the relative velocity voxels of the discretizedmill domain (with a resolution in x, y and z of 46 × 22 × 22
voxels) were considered the output (see Fig. 10).
Adding to the previous approach, a convolutional neuralnet (CNN) was applied. CNNs are an approach often usedin image recognition, in which pooling layers are introduced, with a sliding box sampling and condensing thefeatures of a wider discretized domain, which can be seenin the upper part of Fig. 10. It can be seen that the predicted
result comes close to the real values, which is also evident
in the density distribution curve with close to convergent
curves.
The main advantage of predictive AI as an intermediarymodeling tool lies in its generalizing capabilities. Basedon evaluated generalizing models, AI predictions can beobtained in inference for a large parameter space of highresolution whose systematic simulation, if performed completely, can take significantly longer. In the case of the wet
mill simulation, systematic AI predictions were generated
in the order of seconds, which would have taken time in therange of years with conventional CFD-DEM simulations(Thon et al., 2022a; 2022b).

3.2 Synthesis

AI can be used for the prediction of various particle
properties and required synthesis pathways, as well as forthe investigation of their correlations. Furthermore, AI canbe used for synthesis control and optimization to achieve
desired results.
Energy growth prediction: Chen and Dixon (2018)
developed a machine learning (ML) approach to identify
the relationships between the structure and surface andfragment energy in ZnO nanoparticle morphologies inbottom-up growth using nanoparticle fragments and derived means for the classification and energy expression of
the nanoparticles. The focus was on the ML-based investigation of the stability, phase transition, and growth patternsof clusters, ultra-small nanoparticles, and bulk-sized particles based on fragment geometries and energy parameters.
Shape prediction: Pellegrino et al. (2020) demonstrated
an ML approach for the predictive modeling of TiO2
nanoparticle morphology, the size, polydispersity, andaspect ratio in dependence of process parameters such as
the initial concentrations of Ti (TeoaH)2, additive concentrations of TeoaH3 as the shape controller, pH values,
temperature in a hydrothermal synthesis process. Inversely,
it can be used as a reverse-engineering approach to predict
optimal process conditions for specific desired product11

Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

Review Paper

Max & average
pooling

max pooling46 layers

averagepooling
110 mmLayer 13Layer 18

initialoptimized

test

optimized
test

Probability density distribution [-]initialParticle number distribution/ Histogram [-]Layer 6

Relative velocity [m/s]Fig. 10 Predictive models for relative velocities in a vertical dry stirred media mill with an improvement of the modeling approach by applying
convolutional neural nets with integrated pooling layers. Very accurate models can be trained with heatmaps depicted for an independent evaluationcase (test), with the initial prediction based on backpropagation and the final prediction following GRL. Both the side and top views of heatmaps andthe whole domain density distribution show good alignment (original work by author).

characteristics in terms of aspect ratio, morphology, andsize.
Process control for shape (hybrid): Zhang et al.
(2012) demonstrated a hybrid model to control particle
size distribution in a cobalt oxalate synthesis process,with the first principle model capturing the known processcharacteristics with a partial least-squares (PLS) regression
ML model to compensate for its shortcomings, with the
combined model being capable to model the PSD evolution. The reaction took place in a two-reactor system being
connected in series, with the first acting as an ammonium
oxalate dissolver and the second being responsible for the
successive crystallization.
Process optimization for yield: Jose et al. (2021)
demonstrated that AI in the form of multi-object ML optimization can also be used for the optimization of productyields with the example of ZnO nanoparticle synthesis.
The optimization relied on high-yield microreactors andhigh-throughput analysis. In less than 100 experiments, optimization could be achieved, allowing for the continuous
production of 1 kg per day. A scalability assessment of theapproach was conducted.
Prediction, experimental planning, and synthesis-

12

assistance: A dedicated review article that Tao et al. (2021)
conducted focused on the use of AI in the specific context
of nanoparticle synthesis. Examples were provided for theprediction of nanoparticle properties, the assistance of thesynthesis, experimental planning, and data generation in
the contexts of semiconductor, metal, carbon-based, andpolymer nanoparticles. However, most current applicationsin the field focus on the identification and optimization ofexisting procedures, with some targeting the identificationof novel particles and procedures.

3.3 Stabilization and Functionalization

After or during synthesis (e.g., during in-situ stabilization) and in the context of other unit operations (such as
fine grinding, agglomeration, etc.), stabilization is required,as generated product particles would otherwise agglomerate uncontrollably. Identification of suitable stabilizers canbe a laborious task, with the result and emerging properties
often being unpredictable. Here, AI modeling can be auseful tool in predictive identification and the estimationof resulting properties in regard to solution or adsorption
capabilities and resulting stability.
Prediction of solution capabilities: Astray et al. (2017)

Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

showed an AI model to predict the solution properties of alkylammonium surfactants such as density, surface tension,and kinematic viscosity as functions of input parameterssuch as concentration, carbon number, and the molecularweight of surfactants, with an R2 of 0.97.
Prediction of adsorption capabilities: Faghiri et al.
(2019) trained an AI model based on least-square vector
machines to predict the surfactant adsorption of sandstone
in the context of oil recovery. Parameters were surfactant
density, concentration, time, and kinetic adsorption density.
Good predictive capability could be reached for kinetic adsorption density, with R2 accuracies for independent testingdata of around 0.96.
Prediction of final stability: Kundu et al. (2016)
focused on stabilization in emulsions. A hybrid multiple
inputs and multiple outputs (MIMO) ANN-GA model
was used to predict the formation and stability of (o/w)
petroleum emulsions during stirring, with parameters being
oil and surfactant concentration, stirrer tip speed, and pH
value. Prediction errors were at around five percent.
Li L. et al. (2020) modeled the stability of Al2O3ethylene glycol nanofluids via an ANN in dependence ofparticle size distribution and velocity ratio (the ratio between Brownian particle velocity and gravitational settling
velocity). The nanoparticles in ethylene glycol were magnetically stirred and positioned in an ultrasonic bath. Input
parameters were thermal conductivity, viscosity, massfraction, and temperature. R2 accuracy ranged between
0.98 and 0.99.

3.4 Precipitation

Precipitation is an approach whereby the addition of
agents or changes in other influencing parameters such astemperature in a solution can rapidly force specific materials out of the solution to form defined, mostly amorphous
particles. In slower dissolution, the resulting particles
crystalize. In both cases, the prediction of suitable conditions and the estimation of the result are difficult tasks,
which usually involve much often intuitive manual experimentation to identify equilibrium states, resulting process
kinetics, and the final amount of precipitate.
Equilibrium prediction: Leal et al. (2020) demonstrated an on-demand ML (ODML) algorithm to predictbased on simulation results new chemical equilibrium
states effecting precipitation in fluids on the basis of the already past chemical equilibrium states in reactive transport
simulations to reduce computing costs based on simulation
results.
Kinetics prediction: Nielsen et al. (2020) described an
AI framework for modeling particle processes based on
preexisting mechanistic process knowledge and sensor data
to estimate underlying process kinetics. Three case studiesof flocculation, crystallization, and fracture were described
and modeled for the kinetics of different underlying pro-

Review Paper

cesses such as nucleation, growth, shrinkage, fracture, andfor agglomeration.
Final precipitate prediction: Hoseinian et al. (2020)
demonstrated a hybrid neural network–genetic algorithmwith first principle models for the prediction of ion flotationbased Zn(II) removal.

3.5 Mixing

Most processes require homogenous distributions, although on some occasions, controlled inhomogeneities canbe desirable. Therefore, mixing is an important aspect, also
due to energetic reasons. In particular, mixing parameters
should be chosen to not exaggerate energy intake, which
can cause inefficiencies, excess wear, or product degradation, etc. In addition to the modeling of the process, control
modeling and monitoring are highly significant, as mixing
is, in many processes, a highly influential parameter that
often requires active and dynamic adaptations or continuous control during the process.
Qualitative and quantitative monitoring: Bowler etal. (2020) showed ML-based monitoring of a mixing process with ultrasonic sensors in which classification models
were applied for the general mixing capabilities of materials and regression models for the progress monitoring for
mixing time.
Predictive real-time digital twins: Khaydarov et al.
(2020) demonstrated a hybrid surrogate model withbubble recognition and online video processing in aphysical stirred tank reactor for the enhancement of aparallel-running AI-based surrogate model for a CFD simulation, as depicted in Fig. 11. The trained surrogate model
could be implemented on a near real-time running edgenode, with online AR displaying the result for a human operator. As resource-efficient real-time modeling with direct
feedback to human or machine interfaces is a desirable goal
beyond mixing, the use case is interesting for other unit
operations or plant operations. Furthermore, the given casedemonstrates a quick mostly numerical AI-based digitaltwin for the real-time processing of the inner mechanismsof a parallel-running process, yet with periodic alignment
to physical reality via direct measurement feedback (which
can be a soft-sensor approach).
Mixing additive control prediction: Ittiphalin et al.
(2017) demonstrated the addition of fat to a mixing processof feed pallets for animals. Among others, a backpropagation neural network was trained considering moldsize, total percentage of fat, percentage of additional fat,percentage of fibers, and pallet shelf-life as inputs for theestimation of additional fat to be added. In order to achieve
an optimum process for increasing the production rate anddeteriorating pallet shelf-life with an increased fat content,
optimal fat quantities can be added during the dynamic
mixing process.

13Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

Review Paper

Fig. 11 Online surrogate modeling of a mixing process via a CFD surrogate model with direct feedback via an AR interface to a human operator
running in near real time due to AI assistance. Feedback of the physical process was integrated into the approach via machine vision. Reprinted withpermission from from Ref. (Khaydarov et at., 2020). Copyright 2020 B.V.

3.6 Aggregation

As described in precipitation and stabilization, primaryparticles can aggregate, agglomerate, or flocculate, which
can be desirable under controlled circumstances. As in
the previous operations, AI can assist in the prediction ofsuitable conditions and kinetics or the estimation or results.
In addition to the prediction of specific given processes, it
can also be used to identify suitable novel pathways withinlarge parameter spaces.
Hybrid predictive kinetics modeling: Nielsen et al.
(2020) described an AI framework for modeling particle
processes based on preexisting mechanistic process knowledge and sensor data to estimate the underlying process
kinetic rates of constitutive equations based on state variables and control actions. Depending on the investigatedphenomena in the model, kinetic rates included one ormore of the variables of nucleation, growth, shrinkage,
agglomeration, and breakage. Three case studies, namely
flocculation, crystallization, and fracture, were described
and modeled for the kinetics of different underlying processes such as nucleation, growth, shrinkage, fracture, andagglomeration.
Aggregation pathway identification: Li J. et al. (2019)
described the accelerated synthesis of gold nanoclusters
with AU(III) to Au(I) reduction and subsequent coordination via thiolate ligands for complexation, considering all
relevant parameters to identify likely patterns for the successful synthesis of nanoclusters using deep learning (DL).
Combined structure and pathway prediction: Wahl
et al. (2021) demonstrated a ML-based approach to predictboth the aggregate particles (polyelemental heterostructures) via colloidal self-assembly, as well as the way inwhich they formed, using a technique called a diffuse map.
The approach proved capable of predicting many body
phenomena and long-term assembly dynamics.

14

3.7 Extrusion

Another significant process in particle technology is
extrusion. In this process, particles, and eventually, a fluid
are first conveyed and then mixed. In the second step, thecompression and shearing of a viscous mass under highpressure and temperature take place, then the mass isfinally pressed through a shape-giving opening. The parameters are highly interdependent, and the selection of optimal operating conditions is usually time-consuming andincreasingly costly. Consequently, the quality control of the
extruded material is associated with various challenges, including the control of process parameters such as velocity
and temperature or of the final product quality. Therefore, amore intelligent method needs to be developed. To achieve
this goal, AI methods can be beneficial, in addition to being
able to close the gap between often used simulations andaccurate but costly experiments. In the following extrusion
processes, pure polymers are described as the AI methods
can be used similarly for particle-filled polymers.
Curing property prediction: Marcos et al. (2007)
demonstrated the prediction of the extrusion propertiesof rubber, relying on compound composition and mixing
conditions during extrusion. Despite being applied tomolten rubber in this case, extrusion is often based on
granular material, e.g., in many extrusion processes forbattery production, wherefore the general approach is alsoof interest in the context of particle technology. Online information about specific curing properties can be predicted
via integration into the process, eliminating waiting time
for laboratory results and increasing product traceability.
Velocity control: Martínez-de-Pisón et al. (2010) presented a dynamic model for extrusion velocity control,
which was capable of controlling manufacturing processes
based on previously successful start-ups with data about
pressure, temperature, and velocity. The complexity ofthis task was due to the open-loop control system and theconstant alterations inherent to the process.

Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

Temperature control: Taur et al. (1995) proposeda fuzzy PID temperature controller for the temperature
control of the extruder cylinder using if-then rules andmembership functions.
Tsai and Lu (1998) developed a predictive fuzzy PID
controller with single-loop monitoring for extruder cylinder temperature control.
Quality control: Liu X. et al. (2012) proposed dynamicgray-box modeling based on a soft-sensor approach tocontrol the quality of polymer extrusion. In this article, anattempt was made to use a gray box to provide real-timemonitoring of the quality of the extrudate in order to reduce
the setup time and improve the operation of the system.
In this work, a soft-sensor structure was combined with afeedback observer to estimate the viscosity and control the
final quality of the material.
Simulation-experiment translator: Bajimaya et al.
(2007) used an ANN to predict the process parameters in
extrusion. Simulation analyses are usually unable to take
into account the boundary conditions of the manufacturing process, which is why their results remain mainly
theoretical. On the other hand, conducting experiments is
usually expensive and time-consuming, which is why theuse of AI methods can reduce the gap between simulation
and real data. The trained AI algorithm acted as a transfer
tool to translate the theoretical variables gained from finite
element method (FEM) simulations such as stress, strain,
and shearing into realistic measures for extrusion pressure,temperature, and velocity.

3.8 Classification

Classification has many applications in particle technology that can be performed analytically to determine distributions or properties or as physical separation according
to the respective properties. There exist many separation
categories, with some being easier to implement, such as
the classification of compact particles according to size,e.g., via sieving. For other aspects such as morphology orcomposition, sorting can be difficult. In such cases, manualhuman involvement or application-specific hardcoded
automation is often required, but this can be expensive,
unreliable, and inflexible and can limit the quantities thatcan be processed. Consequentially, the application of AI isa viable alternative in such instances.
Analytical classification with machine vision:Gonçalves et al. (2010) demonstrated an ANN-based technique of analytically classifying microscopic wear particles. Binarized particle images were obtained, and features
such as area, perimeter, width, height, diameter, circularity,
ferret diameter, elongation, and aspect ratio were obtained
via ImageJ. Supervised classification reached accuracies ofaround 95 percent.
Massarelli et al. (2021) used computer vision and ML
to analytically (i.e., with no physical separation involved)

Review Paperclassify and count particles depending on dimensional size
and morphology. The particles could be classified under
supervision according to predetermined groups or without
supervision to act as a measure for sample representativeness and in order to identify hidden features.
Physical separation: In addition to mere analyticalclassification, physical sorting can be performed, as
Kattentidt et al. (2003) demonstrated, for the sorting ofbulk solids. In an online operation, AI-based quality control
was performed with two optical sensors for the separationof recycled glass. Distinguishing features were dimensionality, surface area and volume, texture, morphology,
homogeneity, conductivity, and spectral reflection. Finally,
a separation unit sorted the bulk material into dedicatedfractions.
The AI-based sorting of particles, also in the context ofrecycling, has the potential to be a crucial pillar for a circular economy. For instance, Wilts et al. (2021) demonstrated
AI-based waste sorting via a robotic arm and a sorting beltwith a multitude of sensors (metal detector, high-resolution
RGB camera, NIR sensor, VIS sensor).
Furthermore, Peršak et al. (2020) demonstrated thesorting of transparent plastic granulates based on computer
vision and subsequent pneumatic air separation. The plastic particles had sizes of 2–4 mm, sorting accuracy for 9classes equaled 90 percent, and for two classes, it was 100
percent.

3.9 Characterization and Analysis

In almost all cases, after or during unit operations, thecharacterization of properties and their distribution isrequired. Sometimes, these can easily be gathered, whilein other cases, directly obtaining the required data can bechallenging or impossible due to limitations in measurement equipment, physical constraints, accessibility during
operations, etc. Often, analysis is possible but limited inregard to the executable quantity due to the nature of themeasurement technique or the hardware. In all these cases,
AI can be a useful technique to use with parallel runningpredictive or surrogate models, as these can be enriched bylimited physical information, as previously shown in thecase of mixing (Khaydarov et al., 2020). In addition to use
in soft sensors, AI and evolutionary approaches can be used
to enhance measurement results through post-processing or
to derive mechanical descriptions of the measured properties and their influencing parameters.
AI as a soft sensor: Reliable predictive models can also
be used to directly replace measurement, or as additional
assistance in the context of soft sensors.
An important characterization method for particle breakage is the particle breakage function, which can be determined via a breakage tester, as described in Böttcher et al.
(2021) (see Fig.12).
In the breakage tester, rotational velocity and gap size

15Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

Review Paper

Fig. 12 A predictive model for a breakage tester to characterize the particle breakage function. Successful prediction capabilities were achieved for
acting normal forces, power intake, and resulting particle size distribution. Further processing can be done for transference into existing breakage
function models, with AI enabling reduction in the physical measurement effort (original work by author). Left upper picture reprinted with permission
from Ref. (Böttcher et al., 2021). Copyright 2021 B.V.

can be adjusted, while torque and normal force between therollers are measured. Based on GRL for rotational velocity,with gap size and initial particle size as input parameters,a predictive model was trained for the prediction of power,
force, and resulting particle size via two fitting parameters.
Both, the prediction of power and force achieved highaccuracies, with an R2 in both cases of 0.97.
Sun et al. (2017) successfully trained a three-layer neuralnet for the prediction of electron transfer properties such as
the Femi energy of silver nanoparticles and successfully
identified correlations with structural and morphologicalfeatures.
Measurement improvement: Furat et al. (2022) applied
generative adversarial networks (GANs) to retrospectively
super-resolve images of particle based Li-ion electrodes
for the quantification of features such as the identificationof cracks in aged cathodes. Consequentially, the trade-off
between resolution and field of view in the SEM imagingof bulk-pictures was significantly reduced.
Mechanistic analysis measure modeling: GAs provide
another AI method that is often used for optimization
tasks by mimicking the process of evolution involving themutation, selection, and reproduction of system states andparameters. Finke et al. (2021) used a GA for the identification and optimization of suitable model parameters for a
viscosity model of nanoparticulate suspensions.

3.10 Other Applications

A method with great potential for future applications is

16

a combination of nature-based or mathematical algorithms
and AI.
Drilling and blasting through hybrid models: Onepossible hybrid approach was introduced by Mojtahedi
et al. (2019), which was developed for the prediction ofparticle size distribution in mining and is presented below.
Drilling and blasting are the most widely used methods in mining for rock crushing. In order to describe thefragmentation, the ANFIS method was combined withthe firefly algorithm (FA). By extending a fuzzy inference
system (FIS) with an artificial neural network (ANN), thesystem becomes self-learning and can solve nonlinear
complex problems. In addition, the combination of ANFIS with the FA provides an ability to determine optimalvalues for complex nonlinear problems. More precisely,
the FA describes a nature-based algorithm of firefly social
behavior, where the convergence speed of the algorithm
is high and the number of iterations for an optimal result
is low. The objective of the ANFIS-FA was to predict theD80 (the 80 percent passing size at which 80 percent of theparticles are smaller) value for different input parametersof the resulting particle distribution, using a data set from88 blasting operations at the Shur river dam in Iran. For
further validation of the predictions, the ANFIS-FA method
was contrasted with linear multiple regression (LMR)
methods alongside the test data set.
Six different hybrid models were created, each differingin the choice of input parameters used. Each of these models achieved higher R2 values with a minimum of 0.856 inChristoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

predicting the D80 value than the LMR method, with 0.669
in relation to the test data set.
In summary, combining AI methods with existing algorithms has been demonstrated to significantly improve
the accuracy of particle size predictions (Mojtahedi et al.,2019).

Review Paper

Predictive AI systems have the potential for massive savings in computing power and extensions of the investigableparameter space, yet they represent customized solutions
that need to be performed repeatedly for new use cases, as
was done for the dry operated vertical mill simulation after
the modeling of the wet simulation.
More universally applicable AI solutions exist forsome simulation methods in the form of surrogate models, although development is still in an early state.
Calzolari et al., using tensor basis neural networks(TBNN) and CNNs (discussed in terms of the verticalmill), described CFD surrogate models (see left side ofFig. 13), demonstrating the time extrapolation of flow
fields and AI upscaling based on previously learned
physical data to derive super-resolved fields for lowresolution fields and physics-informed DL turbulencemodel enhancement (Calzolari and Liu, 2021).
For the discrete elements method, the authors (Thonand Schilde) are currently working on a surrogate model
to train small-scale unit cells with particle positions andforces from DEM for incorporation in a respective smallscale unit cell surrogate model for later application on large
domains. By discretizing the large domain cyclically andre-meshing the unit cell grid, the dynamics for larger domains are to be represented by the surrogate model and predicted as a new initialization state for subsequent iteration.
The Initial training of output 3D particle positions overtime is promising, with minuscule errors in individual time
steps and a mean error of 0.5 percent per spatial dimension(0.1 nm error in a 20 × 20 × 20 nm unit cell).

4. AI Surrogate Models

Sophisticated computer simulations are increasingly
aiding particle technology in all covered domains. Depending on the time and length scale, different simulation
techniques can be applied, such as the Finite Elements
Method (FEM) for the investigation of the inner structuraland mechanical behaviors of solid objects, Computer Fluid
Dynamics (CFD) for the simulation of fluid flows, theDiscrete Elements Method (DEM) for the investigationof the interactions among many particles and boundaries,
or population balance simulations (PB) for the numericalmodeling of particle-based processes. The methods canbe coupled for multi-scale investigations or in CFD andDEM to investigate particulate systems within fluids, suchas air or water. Interaction can be mono- or bidirectional,
depending on the required accuracy and computational
demands. Coupled CFD-DEM simulations are often used
to numerically investigate particles in fluidic environments(Li J. and Tao J., 2018; Schrader et al., 2019; Zhu et al.,2020; Beinert et al., 2018; El-Emam et al., 2019). Suchsimulations are often demanding in terms of time and computing resources, limiting the space, time, or accuracy thatcan be investigated.

DEM surrogate models

Inferece on large scale

Training on small scale

CFD surrogate models

Input Positions
 Forces

Small scale simulation

Output New
positions

AISurrogate
Model

Discretization in AI inference and New positioning
moduleinterpolation and discretization

Cyclic Iteration (time steps)

Fig. 13 Depiction of surrogate models for CFD simulations to quickly get accurate results, which would take unaided CFD significantly longer. On
the right side, a current approach for a surrogate model for DEM is shown, in which a predictive model of a 3D tesseract is trained for a small-scale unit
for later iterative inference in a larger discretized domain with remeshing. The left part is a reprint of Calzolari and Liu (2021) under the terms of the
CC-BY 4.0 license. Copyright: (2021) The Authors, published by Elsevier. (The right part is an original work by the author).

17Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

5. AI-based design and scale-up

In addition to modeling, the optimization or control ofindividual processes such as synthesis, grinding, analysis etc., the design, modeling, and control of plants andcomplex networks as a whole, as well as layout and optimization, including the design of the underlying processes
and units, is of major economic significance. Using AI for
design, layout, optimization, or scale-up can lead to significant savings and improved efficiency.
Other than for the task or unit’s specific applications,elaborated on earlier in the publication, adoption of AI inparticle technology in this respect is still in an early stage.
However, there are some examples, more advanced examples, and real-world applications in other fields, with theused approaches being transferable to particle technology,e.g., the use of GAs for the autonomous design of heatexchangers, which can be easily adopted for the design ofmilling geometries.
Generally, two major approaches can be distinguished,
the design and optimization of dedicated production
systems, processes, unit operations, plants, and logistics
infrastructure for dedicated goals or the intelligent dynamic
identification, connection, and management of suitable(potentially decentralized) infrastructure already in existence.
The former method of dedicated design tasks can beused for basic operations or if products will be required in
the long term and in large quantities, making applicationspecific inflexible yet efficient high-throughput systems asuitable option.
With GAs, Wisniewske (2004) demonstrated an optimal
design of reinforcing fibers within a composite, while Jattiet al. (2013) optimized the milling of aluminum alloys. For
the design of process units, RatnaRaju and Nandi (2013)used GAs for the construction of heat exchangers whileconsidering pressure drop and effectiveness.
In contrast to evolutionary techniques, Lee X.Y. et
al. (2019) used deep reinforcement learning for microfluidic flow sculpturing, finding it to be superior to a GA
approach. Similarly, with classical AI techniques suchas neural nets, Sarkar et al. (2019) demonstrated an autonomous design of a compressor rotor and optimization
through AL.
Specifically, GANs, in which two competing AIs traineach other, are useful for the design process. In this approach, a generator learns from training examples in orderto produce similar results, while a discriminator AI evaluates the results and learns through feedback to improve theaccuracy of its evaluation. Being mostly known from deep
fakes, the generation of realistic images, they can also be
applied to generate solutions for given problems. Via theiterative interaction of generator and critique AI, the two
networks improve each other’s capabilities.
Chen and Fuge (2019) demonstrated GANs for design

18

Review Paper

synthesis, the generation of hierarchical designs in conjunction with generative models. Similarly, Oh et al. (2019)
applied GANs for the generation of 2D wheels. Nobari etal. (2021) demonstrated autonomous design synthesis forconstrained 3D geometry generation for different purposes
via deep generative models and constraint GANs.
In addition to the complete execution of the design
process, AI can be used in a semi-automated manner toassist manual design endeavors. For instance, Khan et al.
(2019) discussed iterative design adjustment in the construction endeavor in a loop with user feedback. Similarly,
Deshpande and Purwar (2019) demonstrated the solution
generation of mechanisms and synthesis for abstract inputs.
Raina et al. (2019) applied transfer learning to incorporate historical design strategies from past cases into
AI systems. Combining these approaches in the future, aclosed loop of adjustment with continued learning based onhuman feedback can be implemented.
Similarly, GA-, ANN-, and GAN-based design or theevolution of design strategies can be applied for processesor production units in particle technology, such as in thedesign of mill geometries and operating conditions or forthe morphological features of product particles.
In regard to the second major variant in design, optimization and scale-up, the intelligent management and interconnection of existing infrastructures, units, systems, andprocesses, a key foundation are digital twins, which can bepredictive AI models, as already discussed in this publication. Cyber-physical systems, which include sensors, communication, computation, and control elements, are closely
associated with digital twins as virtual representations ofphysical systems. Ideally, they rely on a digital twin formonitoring, control, and autonomous decision-making(Lee E.A., 2008; Tao et al., 2019). Together with cloudcomputing, big data, and the internet of things (IoT) as thefoundation, both cyber physical systems and digital twins,
which can rely heavily on AI, are important components ofthe concept of smart factories and industry 4.0.
As already discussed, in a fixed production environment,
management and control are vital for the dynamic interconnection and allocation of resources, units, processes,
or entire plants in a decentralized (potentially global)
manner while considering the available infrastructure andits flexibility, intelligent supply chain management anddecision-making are of paramount importance. Toorajipour
et al. (2021) performed a more detailed review of AI
in supply chain management and successive decisionmaking, focusing on marketing (including sales, pricing,segmentation, consumer behavior, decision support, direct
marketing, industrial marketing, new products, etc.), logistics (containing container terminal operations, inbound,logistics automation, sizing, etc.), production (assembly,
integration, product driven control, etc.), and the supply
chain itself (facility location, supplier selection, supplyChristoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

chain network design, risk management, and inventory replenishment), with references to specific examples. Supply
chain management and autonomous decision making areimportant aspects that mirror, to a large degree, processcontrol in a fixed system, although challenges in a dynamic
decentralized system can strongly outweigh those in a fixedsystem.
In terms of the online monitoring of large connected
production systems, the company Hosokawa Alpine (2019)
applies an ML-based platform intelligent software assistant(ISA) for the maximization of efficiency and quality.
Regarding the supply chain network design underconsideration of profit maximization, Zhang et al. (2017)
demonstrated a competition-based intelligent physarum
solver.
Wang et al. (2012) demonstrated autonomous collaboration between heterogeneous actors (multi-robotic systems)
and AI to solve complex formation performance in a dynamic environment.
In regard to the dynamics of product life-cycles andsupply chain networks and the resulting inbound fluctuations, Knoll et al. (2016) demonstrated predictive inboundlogistics planning.
Kwon et al. (2007) presented an integrated multi-agent
framework to solve collaboration tasks in supply chain
management in regard to supply and demand uncertainties.
In cases when human supervision is desired, Kasie et al.
(2017) demonstrated a decision support system combining
AI and discrete-event simulations (DES), as well as database management.
Applied to particle technology, the existing production
sites addressed earlier in this review can be intelligently
connected in a dynamic, decentralized, self-organizing
structure based on digital twins and cyber physical systems. Based on AI market prediction, as well as autonomous real-time monitoring and decision making, the costand efficiency benefits of smart factories and industry 4.0
can be adopted more progressively in the field of particle
technology.

6. Conclusion

In conclusion, AI techniques are already used in allareas of particle technology. Predictive modeling, often thefoundation for successive AI applications or conventional
post-processing, is already universally represented for allprocess types, although the degree of adoption varies. AI
methods or evolutionary approaches, which are often usedin combination, are furthermore applied for the optimization or set up of process control.
AI can perform or assist in scale-up, as trained AI
models can be transferred between different scales throughpre-training. In regard to classical simulations such as
DEM, FEM, or CFD, AI can aid in numerical investigation.
Surrogate models are increasingly being investigated in

Review Paper

various fields as quick-to-apply alternatives to classicalsimulations such as DEM for particulate systems. Often,
the simulation counterpart is used as a training foundation
for the training of surrogate models. Consequently, throughsurrogate models, the investigation of systems such as large
stirred media mills with trillions or quadrillions of particles
or more and their inner workings can be possible, ordersof magnitudes being larger than what could be done withstate-of-the-art simulations. Inversely, surrogate models
can be performed significantly faster than real simulations,allowing for real-time or faster numerical investigationsof parallel-running physical processes or plants, allowing
for the predictive control of complex milling or reaction
processes or of entire plants. Since simplifications or approximations need to be done often as compromises need
to be made between accuracy and computing performance,e.g., simpler contact models or focusing on grinding beads
without product particles, such simplifications can become
unnecessary with surrogate models, allowing for greatertransparency and realism.
Regarding the criticism of AI’s black-box nature, there
are techniques to increase the transparency of AI models,in particular the use of genetic programming (GP) or
reverse-engineering strategies, to transfer AI models into
more transparent representations to facilitate, among
others, the mechanistic modeling endeavor in particle
technology.
In summary, there are examples in most domains ofparticle technology, although widespread adoption has not
yet been achieved. Based on the existing examples, ranging
from proof of principles to successfully applied economic
adoptions, with the exponential increase in general capability, AI adoption will be a major determinant of economic
competitiveness and survivability in this and the following
decade.

7. Outlook

Looking towards the future, the author sees promisingareas and ideas to address untapped potential and remaining shortcomings.
In Fig. 14, a possible framework is depicted, extending apreviously published hybrid modeling approach now called
hybrid regression evolutionary network (HyREN) (Thon etal., 2022b).
1) HyREN: Being marked by the blue square, training
data from either experiments or experimentally validated
simulations (e.g., via DEM calibration or the execution andcomparison of identical mill experiments and simulations)
are used for parallel hybrid modeling, also called gray-box
modeling. In this way, the parallel-running black-box AI
only models the divergence between predefined white-box
models, e.g., an existing physical or mechanistic modelfrom the literature, and only compensates for the whitebox model’s shortcomings, with major tendencies being

19

Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

Review Paper

Fig. 14 Outlook for a comprehensive modeling system combining classical AI techniques such as neural nets with evolutionary approaches such as
genetic algorithms and programming in conjunction with hybrid modeling. Integrated optimization and the establishment of control decision trees canbe generated based on underlying hybrid models acting as digital twins. Furthermore, dimensionality analysis, equations, and physics recognition canbe added to improve the underlying modeling capabilities and improve transparency and physical reasonability. The middle blue box is a reprint fromThon et al. (2021), as are the lower boxes for genetic algorithms and programming (Thon et al., 2022b), both under the terms of the CC-BY 4.0 license.
Copyright: (2021) The Authors, published by Wiley.

defined by the transparent and established white-box model.
Successive GPs can be performed to break down the blackbox model component into more transparent representations to iteratively extend the white-box model. Dependingon whether human intervention will be involved, the extensions can be empirical or mechanistic. Based on this as an
outlook, several additional extensions are proposed, which
are currently being developed.
2) Genetic reinforcement learning: One shortcomingof AI models is usually their large training data requirements. The GRL training technique has already addressed
this shortcoming. Despite GRL’s advantages, unknown
regions in the parameter space in terms of the available
training data can still compromise the generalizing capabilities of the model.
3) Active search and request: e.g., the comparison of
the systematic predictions of independently trained AIs,
can reveal parameter combinations of uncertainty in orderto close these gaps with a minimum amount of additional
data acquisition (via targeted simulations or experiments).
In regard to hybrid modeling and the iterative extensionof white-box components, additional approaches can increase physical significance.
4) Dimensionality analysis: As shown in green, GP canbe augmented through dimensionality analysis to ensure
the mathematical and physical consistency of units.
5) AI-driven regression: Furthermore, initialization
with suitable equations, derived from AI detection basedon the (n-dimensional) shape of the data, can facilitate the20

process.
6) AI-driven physics recognition: Furthermore, empirical models can be transferred to mechanistic models
in a semi-automated manner by generating a large poolof equations containing known physical or mechanistic
equations. Resulting from generations of crossover andmutations, and involving tree structures from this equationpool, recognition AIs can be trained to later be applied
to the identification of white-box equations or respective
fragments in data from real use cases.
7) Digital twins for optimization and control: Based on
the trained models (black, gray, or white box), subsequent
techniques such as GA, GP, or AL, etc. can be performed
and considered to be safe to interact with and quickto-apply virtual process representations (AI-based digital
twins) for optimization and control.
8) Online Learning: Further development opportunities
and the application of data-driven methods in process,formulation, and material modeling are manifold, such as
online learning in which AI is constantly running in parallel
to processes or data banks to update its internal modelingand dynamically adapt to changing circumstances (e.g.,anomalies such as the blocking of particles in a grinding
process or changes in the product formulation). Similar toGAs, ALs allow for the optimization of defined goals.
9) Autonomous AI modularization: Via un- or selfsupervised learning, the use of modular neural nets or
dropout, automated identification and the decomposition of process and production engineering issues into

Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

sub-processes and sub-mechanisms for better (hybrid)modeling and control of the entire process chain can beachieved. For instance, a complex production plant withvarious sensors and actuators can be autonomously broken
down into closed unit systems, which can then be interconnected to represent the entire plant, simplifying the modeling effort while increasing the transparency of the model.
10) An Expert Management System for AI: As
different AI tools (neural nets, evolutionary algorithms,
Hierarchical Hidden Markov models, etc.) provide varying
advantages and disadvantages, the best results can usually
be achieved via the intelligent combination of methods
either in the form of joint applications or the fusion of
methods (ensemble methods). In the future, based on a comprehensive toolbox of methods, a self-supervised expertmanagement system can be established for the optimal
allocation and interconnection of ML methods.
Since there exist real-world examples in particle technology of almost all unit operations in terms of predictivemodeling and, increasingly, optimization and control
via AI, the mostly untapped design, scale-up, modeling,
optimization, control, and monitoring of large, highly connected systems such as entire plants or decentralized production facilities are likely to be the next steps in adoption.
11) AI-driven unit and geometry design: Furthermore,
the design of individual components such as stirrer geometries can be facilitated. That is, the geometry design itself
can be performed numerically, e.g., via AL or the use of
GAs along predefined constraints, as can be the optimization of process parameters.
12) Surrogate models: Through the use of surrogate
models, quick iteration and prototyping are possible. Forprocess control, AI models can be used as quick-to-apply
digital twins (predictive/surrogate models) that run fasterthan real time and have the potential to improve automationand the autonomous development of sophisticated control
schemes.
13) Brain-derived AI: Finally, current AI is still loosely
inspired by biological nerve systems. Recent developments in neuroscience and the successive adoption of AI,
specifically in regard to neuroscientific theory about the
mammalian neocortex, as found in Hawkins et al. (Holeand Ahmad, 2021; Hawkins and Ahmad, 2016; Hawkins
et al., 2017, 2018; Lewis et al., 2019), can allow for moregeneral levels of intelligence compared to the currently
narrow AI. These advancements harbor the potential toallow for desired features currently investigated in thecontext of classical AI such as online learning (permanent model update), distributed voting AIs (robustness,
generalizing capabilities), or reference frames (Hinton,2022). This can allow for more general AI systems, which
can apply and detect learned patterns, strategies, andsolutions in the context of novel or other types of unit
operations via analogies. Applied to particle technol-

Review Paperogy, being a wide field involved in many industries, theautonomous transfer and detection of common patterns
and solutions can allow for the identification of nonobvious solutions, e.g., the use of Michaelis–Menten kinetics for the dispersion of nanoparticles (Schilde et al., 2010).

Acknowledgments

We would like to thank the company ESSS (Florianópolis,
Brasil) as the developer of Rocky DEM for the close
research cooperation and provision of the research license. We especially thank Jan-Phillipp Fürstenau of
CADFEM GmbH (Hanover, Germany) for the technical support. The authors want to thank the Deutsche
Forschungsgemeinschaft (DFG) in the context of the
Heisenberg Program SCHI 1265/19-1 “Digital methods forcomplex systems in process and production engineering”
and the SPP 2364 “Autonomous Processes in Particle
Technology”.

Nomenclature
AI
ANFIS
ANN
ASIC
BP
BPNN
CFD
CNN
DEM
DL
DNN
FA
FEM
FIS
GA
GAM
GAN
GP
GRL
IoT
LIME
LMR
LSTM
MIMO
ML
ODML
PB
PID
PLS
PSD
RVLFN
SEM

Artificial intelligence
Adaptive neuro-fuzzy inference system
Artificial neural network
Application-specific integrated circuit
Backpropagation
Back propagation neural network
Computer fluid dynamic
Convolutional neural network
Discrete elements method
Deep learning
Deep neural network
Firefly algorithm
Finite elements method
Fuzzy inference system
Genetic (or evolutionary) algorithm
Generalized additive model
Generative adversarial network
Genetic programming
Genetic reinforcement learning
Internet of things
Local interpretable model agnostic explorations
Linear multiple regression
Long short-term memory
Multiple inputs and multiple outputs
Machine learning
On-demand machine learning
Population balance
Proportional integral derivative (controller)
Partial least-squares regression
Particle size distributions
Random vector functional-link network
Scanning electron microscope

21Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25
TBNN
XAI
YOLO

Tensor basis neural networks
Explainable AI
You only look once (object detection)

References

Ashraf C., Joshi N., Beck D.A.C., Pfaendtner J., Data science in chemicalengineering: applications to molecular science, Annual Review ofChemical and Biomolecular Engineering, 12 (2021) 15–37. DOI:
10.1146/annurev-chembioeng-101220-102232
Astray G., Mejuto J.C., Approach of different properties of alkylammonium surfactants using artificial intelligence and response surface
methodology, Tenside Surfactants Detergents, 54 (2017) 132–140.
DOI: 10.3139/113.110483
Bajimaya S.M., Park S., Wang G.-N., Predicting extrusion process parameters using neural networks, International Journal of Mechanicaland Mechatronics Engineering, 1 (2007) 644–648. DOI: 10.5281/
zenodo.1061615
Beinert S., Fragnière G., Schilde C., Kwade A., Multiscale simulationof fine grinding and dispersing processes: stressing probability,
stressing energy and resultant breakage rate, Advanced Powder
Technology, 29 (2018) 573–583. DOI: 10.1016/j.apt.2017.11.034
Böttcher A.-C., Thon C., Fragnière G., Chagas A., Schilde C., Kwade A.,
Rigidly-mounted roll mill as breakage tester for characterizing fineparticle breakage, Powder Technology, 383 (2021) 554–563. DOI:
10.1016/j.powtec.2021.01.055
Bowler A.L., Bakalis S., Watson N.J., Monitoring mixing processes using
ultrasonic sensors and machine learning, Sensors, 20 (2020) 1813.
DOI: 10.3390/s20071813
Calzolari G., Liu W., Deep learning to replace, improve, or aid CFD analysis in built environment applications: a review, Building and Environment, 206 (2021) 108315. DOI: 10.1016/j.buildenv.2021.108315
Chen M., Dixon D.A., Machine-learning approach for the developmentof structure–energy relationships of ZnO nanoparticles, The Journalof Physical Chemistry C, 122 (2018) 18621–18639. DOI: 10.1021/
acs.jpcc.8b01667
Chen W., Fuge M., Synthesizing designs with interpart dependencies
using hierarchical generative adversarial networks, Journal of
Mechanical Design, 141 (2019) 111403. DOI: 10.1115/1.4044076
Dai W., Liu Q., Chai T., Particle size estimate of grinding processes
using random vector functional link networks with improved
robustness, Neurocomputing, 169 (2015) 361–372. DOI: 10.1016/j.
neucom.2014.08.098
Demir-Kavuk O., Kamada M., Akutsu T., Knapp E.-W., Prediction using
step-wise L1, L2 regularization and feature selection for small data
sets with large number of features, BMC Bioinformatics, 12 (2011)
412. DOI: 10.1186/1471-2105-12-412
Deshpande S., Purwar A., Computational creativity via assisted variational
synthesis of mechanisms using deep generative models, Journal of
Mechanical Design, 141 (2019) 121402. DOI: 10.1115/1.4044396
El-Emam M.A., Shi W., Zhou L., CFD-DEM simulation and optimizationof gas-cyclone performance with realistic macroscopic particulatematter, Advanced Powder Technology, 30 (2019) 2686–2702. DOI:
10.1016/j.apt.2019.08.015
Evans B.P., Xue B., Zhang M., What’s inside the black-box?: a genetic
programming method for interpreting complex machine learningmodels, Proceedings of the Genetic and Evolutionary Computation
Conference, (2019) 1012–1020. DOI: 10.1145/3321707.3321726
Faghihi S., Keykhosravi A., Shahbazi K., Modeling of kinetic adsorption of natural surfactants on sandstone minerals: spotlight onaccurate prediction and data evaluation, Colloid and Interface
Science Communications, 33 (2019) 100208. DOI: 10.1016/j.
colcom.2019.100208
Finke B., Sangrós Giménez C., Kwade A., Schilde C., Viscosity model for

22

Review Paper

nanoparticulate suspensions based on surface interactions, Materials(Basel, Switzerland), 14 (2021) 2752. DOI: 10.3390/ma14112752
Furat O., Finegan D.P., Yang Z., Kirstein T., Smith K., Schmidt V.,
Super-resolving microscopy images of Li-ion electrodes forfine-feature quantification using generative adversarial networks,
NPJ Computational Materials, 8 (2022) 68. DOI: 10.1038/s41524022-00749-z
Gonçalves V.D., De Almeida L.F., Mathias M.H., Wear particle classifiersystem based on an artificial neural network, Strojniški vestnik–
Journal of Mechanical Engineering, 56 (2010) 277–281.
Hawkins D.M., The problem of overfitting, Journal of Chemical Information and Computer Sciences, 44 (2004) 1–12. DOI: 10.1021/
ci0342472
Hawkins J., Ahmad S., Why neurons have thousands of synapses, a theoryof sequence memory in neocortex, Frontiers in Neural Circuits, 10(2016) 23. DOI: 10.3389/fncir.2016.00023
Hawkins J., Ahmad S., Cui Y., A theory of how columns in the neocortex
enable learning the structure of the world, Frontiers in Neural Circuits, 11 (2017) 81. DOI: 10.3389/fncir.2017.00081
Hawkins J., Lewis M., Klukas M., Purdy S., Ahmad S., A framework
for intelligence and cortical function based on grid cells in the neocortex, Frontiers in Neural Circuits, 12 (2018) 121. DOI: 10.3389/
fncir.2018.00121
Hinton G., How to represent part-whole hierarchies in a neural network,
Neural Computation, (2022) 1–40. DOI: 10.1162/neco_a_01557
Hole K.J., Ahmad S., A thousand brains: toward biologically constrained
AI, SN Applied Sciences, 3 (2021) 743. DOI: 10.1007/s42452-02104715-0
Hoseinian F.S., Rezai B., Kowsari E., Safari M., A hybrid neural network/genetic algorithm to predict Zn(II) removal by ion flotation,
Separation Science and Technology, 55 (2020) 1197–1206. DOI:
10.1080/01496395.2019.1582543
Hosokawa Alpine ( Hosokawa Alpine AG), 2019, ISA von Hosokawa
Alpine steigert effizienz in der produktion <https://www.hosokawaalpine.de/unternehmen/news/detail/isa-steigert-anlageneffizienz/>
accessed 04.11.2022.
Ittiphalin M., Arnonkijpanich B., Pathumnakul S., An artificial intelligence model to estimate the fat addition ratio for the mixing process
in the animal feed industry, Journal of Intelligent Manufacturing, 28(2017) 219–228. DOI: 10.1007/s10845-014-0972-x
Jatti V.S., Sekhar R., Patil R.K., Study of ball nose end milling of LM6
al alloy: surface roughness optimisation using genetic algorithm,
International Journal of Engineering and Technology (IJET), 5(2013) 2859–2865.
Jose N.A., Kovalev M., Bradford E., Schweidtmann A.M., Chun Zeng H.,
Lapkin A.A., Pushing nanomaterials up to the kilogram scale – Anaccelerated approach for synthesizing antimicrobial ZnO with highshear reactors, machine learning and high-throughput analysis,
Chemical Engineering Journal, 426 (2021) 131345. DOI: 10.1016/j.
cej.2021.131345
Kasie F.M., Bright G., Walker A., Decision support systems in manufacturing: a survey and future trends, Journal of Modelling inManagement, 12 (2017) 432–454. DOI: 10.1108/JM2-02-2016-0015
Kattentidt H.U.R., Jong T.P.R. de, Dalmijn W.L., Multi-sensor identification and sorting of bulk solids, Control Engineering Practice, 11(2003) 41–47. DOI: 10.1016/S0967-0661(02)00140-5
Khan S., Gunpinar E., Moriguchi M., Suzuki H., Evolving a psychophysical distance metric for generative design exploration of diverse
shapes, Journal of Mechanical Design, 141 (2019) 111101. DOI:
10.1115/1.4043678
Khaydarov V., Heinze S., Graube M., Knüpfer A., Knespel M.,
Merkelbach S., Urbas L., From stirring to mixing: artificial intelligence in the process industry, 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA),(2020). DOI: 10.1109/ETFA46521.2020.9212018

Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25
Knoll D., Prüglmeier M., Reinhart G., Predicting future inbound logistics processes using machine learning, Procedia CIRP, 52 (2016)
145–150. DOI: 10.1016/j.procir.2016.07.078
Krishna C.V., Rohit H.R., Mohana, A review of artificial intelligence
methods for data science and data analytics: applications andresearch challenges, 2018 2nd International Conference on I-SMAC(IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). (2018)591–594. DOI: 10.1109/I-SMAC.2018.8653670
Kundu P., Paul V., Kumar V., Mishra I.M., An adaptive modelingof petroleum emulsion formation and stability by a heuristic
multiobjective artificial neural network-genetic algorithm,
Petroleum Science and Technology, 34 (2016) 350–358. DOI:
10.1080/10916466.2015.1135169
Kwon O., Im G.P., Lee K.C., MACE-SCM: a multi-agent and case-based
reasoning collaboration mechanism for supply chain management
under supply and demand uncertainties, Expert Systems with
Applications, 33 (2007) 690–705. DOI: 10.1016/j.eswa.2006.06.015
Leal A.M.M., Kyas S., Kulik D.A., Saar M.O., Accelerating reactive
transport modeling: on-demand machine learning algorithm forchemical equilibrium calculations, Transport in Porous Media, 133(2020) 161–204. DOI: 10.1007/s11242-020-01412-1
LeCun Y., Bengio Y., Hinton G., Deep learning, Nature, 521 (2015)
436–444. DOI: 10.1038/nature14539
Lee E.A., Cyber physical systems: design challenges, 2008 11th IEEE
International Symposium on Object and Component-Oriented
Real-Time Distributed Computing (ISORC), (2008) 363–369. DOI:
10.1109/ISORC.2008.25
Lee J.H., Shin J., Realff M.J., Machine learning: overview of the recent
progresses and implications for the process systems engineering
field, Computers & Chemical Engineering, 114 (2018) 111–121.
DOI: 10.1016/j.compchemeng.2017.10.008
Lee X.Y., Balu A., Stoecklein D., Ganapathysubramanian B., Sarkar S.,
A case study of deep reinforcement learning for engineering design:
application to microfluidic devices for flow sculpting, Journal of
Mechanical Design, 141 (2019) 111401. DOI: 10.1115/1.4044397
Lewis M., Purdy S., Ahmad S., Hawkins J., Locations in the neocortex:a theory of sensorimotor object recognition using cortical gridcells, Frontiers in Neural Circuits, 13 (2019) 22. DOI: 10.3389/
fncir.2019.00022
Li J., Chen T., Lim K., Chen L., Khan S.A., Xie J., Wang X., Deep learning accelerated gold nanocluster synthesis, Advanced Intelligent
Systems, 1 (2019) 1900029. DOI: 10.1002/aisy.201900029
Li J., Tao J., CFD-DEM Two-way coupled numerical simulationof bridge local scour behavior under clear-water conditions,
Transportation Research Record, 2672 (2018) 107–117. DOI:
10.1177/0361198118783170
Li L., Zhai Y., Jin Y., Wang J., Wang H., Ma M., Stability, thermal
performance and artificial neural network modeling of viscosity andthermal conductivity of Al2O3-ethylene glycol nanofluids, Powder
Technology, 363 (2020) 360–368. DOI: 10.1016/j.powtec.2020.01.
006
Linkens D.A., AI in control systems engineering, The Knowledge
Engineering Review, 5 (1990) 181–214. DOI: 10.1017/
S0269888900005403
Liu X., Li K., McAfee M., Nguyen B.K., McNally G.M., Dynamicgray-box modeling for on-line monitoring of polymer extrusion
viscosity, Polymer Engineering & Science, 52 (2012) 1332–1341.
DOI: 10.1002/pen.23080
Marcos A.G., Pernía Espinoza A.V., Elías F.A., Forcada A.G., A neuralnetwork-based approach for optimising rubber extrusion lines, International Journal of Computer Integrated Manufacturing, 20 (2007)
828–837. DOI: 10.1080/09511920601108808
Martínez-de-Pisón F.J., Pernía A.V., Blanco J., González A., Lostado R.,
Control model for an elastomer extrusion process obtained via acomparative analysis of data mining and artificial intelligence tech-

Review Paperniques, Polymer-Plastics Technology and Engineering, 49 (2010)
779–790. DOI: 10.1080/03602551003749585
Massarelli C., Campanale C., Uricchio V.F., A handy open-source application based on computer vision and machine learning algorithms
to count and classify microplastics, Water, 13 (2021) 2104. DOI:
10.3390/w13152104
Mavrovouniotis M., Artificial Intelligence in process engineering, Academic Press Inc, 2012, ISBN: 9780124315129.
McKay B., Lennox B., Willis M., Barton G.W., Montague G., Extruder
modelling: a comparison of two paradigms, UKACC International
Conference on Control ’96 (Conf Publ No 427), (1996). DOI:
10.1049/cp:19960643
Mojtahedi S.F.F., Ebtehaj I., Hasanipanah M., Bonakdari H., Amnieh
H.B., Proposing a novel hybrid intelligent model for the simulationof particle size distribution resulting from blasting, Engineering with
Computers, 35 (2019) 47–56. DOI: 10.1007/s00366-018-0582-x
Nielsen R.F., Nazemzadeh N., Sillesen L.W., Andersson M.P., Gernaey
K.V., Mansouri S.S., Hybrid machine learning assisted modelling
framework for particle processes, Computers & Chemical Engineering, 140 (2020) 106916. DOI: 10.1016/j.compchemeng.2020.106916
Nirschl H., Winkler M., Sinn T., Menesklou P., Autonomous processes inparticle technology, Chemie Ingenieur Technik, 94 (2022) 230–239.
DOI: 10.1002/cite.202100059
Nobari A.H., Chen W., Ahmed F., Range-constrained generative adversarial network: design synthesis under constraints using conditional
generative adversarial networks, Journal of Mechanical Design, 144(2021) 021708. DOI: 10.1115/1.4052442
Oh S., Jung Y., Kim S., Lee I., Kang N., Deep generative design: integration of topology optimization and generative models, Journal of
Mechanical Design, 141 (2019) 111405. DOI: 10.1115/1.4044229
Ongsulee P., Artificial intelligence, machine learning and deep learning,2017 15th International Conference on ICT and Knowledge Engineering (ICT&KE), (2017). DOI: 10.1109/ICTKE.2017.8259629
Patel A.R., Ramaiya K.K., Bhatia C.V., Shah H.N., Bhavsar S.N., Artificial intelligence: prospect in mechanical engineering field—a review,
Data Science and Intelligent Applications, (2021) 267–282. DOI:
10.1007/978-981-15-4474-3_31
Pellegrino F., Isopescu R., Pellutiè L., Sordello F., Rossi A.M., Ortel E.,
Martra G., Hodoroaba V.-D., Maurino V., Machine learning approachfor elucidating and predicting the role of synthesis parameters on the
shape and size of TiO2 nanoparticles, Scientific Reports, 10 (2020)
18910. DOI: 10.1038/s41598-020-75967-w
Peršak T., Viltužnik B., Hernavs J., Klančnik S., Vision-based sorting systems for transparent plastic granulate, Applied Sciences, 10 (2020)
4269. DOI: 10.3390/app10124269
Pham D.T., Pham P.T.N., Artificial intelligence in engineering, International Journal of Machine Tools and Manufacture, 39 (1999)
937–949. DOI: 10.1016/S0890-6955(98)00076-5
Pilania G., Machine learning in materials science: from explainablepredictions to autonomous design, Computational Materials Science,
193 (2021) 110360. DOI: 10.1016/j.commatsci.2021.110360
Psichogios D.C., Ungar L.H., A hybrid neural network-first principles
approach to process modeling, AIChE Journal, 38 (1992) 1499–
1511. DOI: 10.1002/aic.690381003
Qu X.Y., Zeng P., Xu C.C., Fu D.D., RNN-based method for fault
diagnosis of grinding system, 2017 IEEE 7th Annual International
Conference on CYBER Technology in Automation, Control, andIntelligent Systems (CYBER), (2017). 673–678. DOI: 10.1109/
CYBER.2017.8446348
Rahmani A.M., Azhir E., Ali S., Mohammadi M., Ahmed O.H., Yassin
Ghafour M., Hasan Ahmed S., Hosseinzadeh M., Artificial
intelligence approaches and mechanisms for big data analytics: asystematic study, PeerJ Computer Science, 7 (2021) e488. DOI:
10.7717/peerj-cs.488
Raina A., Cagan J., McComb C., Transferring design strategies from23

Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25
human to computer and across design problems, Journal of Mechanical Design, 141 (2019) 114501. DOI: 10.1115/1.4044258
Raschka S., Patterson J., Nolet C., Machine learning in python: maindevelopments and technology trends in data science, Machine
Learning, and Artificial Intelligence, Information, 11 (2020) 193.
DOI: 10.3390/info11040193
RatnaRaju L., NandiT.K., Design optimization of perforated plate
heat exchangers using genetic algorithm, International Journal ofAdvanced Materials Manufacturing and Characterization, 3 (2013)
89–93. DOI: 10.11127/ijammc.2013.02.016
Rupali M., Amit P., A review paper on general concepts of “artificial intelligence and machine learning”, International Advanced Research
Journal in Science, Engineering and Technology, 4 (2017) 79–82.
DOI: 10.17148/iarjset/nciarcse.2017.22
Sakunthala S., Kiranmayi R., Mandadi P.N., A review on artificial
intelligence techniques in electrical drives: neural networks, fuzzy
logic, and genetic algorithm, 2017 International Conference on
Smart Technologies for Smart Nation (SmartTechCon), (2017). DOI:
10.1109/SmartTechCon.2017.8358335
Sarkar S., Mondal S., Joly M., Lynch M.E., Bopardikar S.D., Acharya
R., Perdikaris P., Multifidelity and multiscale Bayesian frameworkfor high-dimensional engineering design and calibration, Journal of
Mechanical Design, 141 (2019) 121001. DOI: 10.1115/1.4044598
Schaffer C., Selecting a classification method by cross-validation,
Machine Learning, 13 (1993) 135–143. DOI: 10.1007/BF00993106
Schilde C., Kampen I., Kwade A., Dispersion kinetics of nano-sizedparticles for different dispersing machines, Chemical Engineering
Science, 65 (2010) 3518–3527. DOI: 10.1016/j.ces.2010.02.043
Schrader M., Pommerehne K., Wolf S., Finke B., Schilde C., Kampen I.,
Lichtenegger T., Krull R., Kwade A., Design of a CFD-DEM-based
method for mechanical stress calculation and its application to glass
bead-enhanced cultivations of filamentous Lentzea aerocolonigenes,
Biochemical Engineering Journal, 148 (2019) 116–130. DOI:
10.1016/j.bej.2019.04.014
Schweidtmann A.M., Esche E., Fischer A., Kloft M., Repke J.-U.,
Sager S., Mitsos A., Machine learning in chemical engineering: aperspective, Chemie Ingenieur Technik, 93 (2021) 2029–2039. DOI:
10.1002/cite.202100083
Srivastava N., Hinton G., Krizhevsky A., Sutskever I., Salakhutdinov R.,
Dropout: a simple way to prevent neural networks from overfitting,
The Journal of Machine Learning Research, 15 (2014) 1929–1958.
Stosch M. von, Oliveira R., Peres J., Feyo de Azevedo S., Hybrid
semi-parametric modeling in process systems engineering: past,present and future, Computers & Chemical Engineering, 60 (2014)
86–101. DOI: 10.1016/j.compchemeng.2013.08.008
Sun B., Fernandez M., Barnard A.S., Machine learning for silver nanoparticle electron transfer property prediction, Journal of Chemical
Information and Modeling, 57 (2017) 2413–2423. DOI: 10.1021/
acs.jcim.7b00272
Tao F., Sui F., Liu A., Qi Q., Zhang M., Song B., Guo Z., Lu S.C.-Y., Nee
A.Y.C., Digital twin-driven product design framework, International
Journal of Production Research, 57 (2019) 3935–3953. DOI:
10.1080/00207543.2018.1443229
Tao H., Wu T., Aldeghi M., Wu T.C., Aspuru-Guzik A., Kumacheva
E., Nanoparticle synthesis assisted by machine learning, Nature
Reviews Materials, 6 (2021) 701–716. DOI: 10.1038/s41578-02100337-5
Taur J.S., Tao C.W., Tsai C.C., Temperature control of a plastic extrusion
barrel using PID fuzzy controllers, Proceedings IEEE Conference on
Industrial Automation and Control Emerging Technology Applications, (1995) 370–375. DOI: 10.1109/IACET.1995.527590
Thon C., Böttcher A.-C., Möhlen F., Yu M., Kwade A., Schilde C.,
Multi-modal framework to model wet milling through numerical

24

Review Paper

simulations and artificial intelligence (part 1), Chemical Engineering
Journal, 449 (2022a) 137794. DOI: 10.1016/j.cej.2022.137794
Thon C., Böttcher A.-C., Möhlen F., Yu M., Kwade A., Schilde C.,
Multi-modal framework to model wet milling through numericalsimulations and artificial intelligence (part 2), Chemical Engineering
Journal, 450 (2022b) 137947. DOI: 10.1016/j.cej.2022.137947
Thon C., Finke B., Kwade A., Schilde C., Artificial Intelligence in Process
Engineering, Advanced Intelligent Systems, 3 (2021) 2000261. DOI:
10.1002/aisy.202000261
Tie M., Yue H., Chai T., A hybrid intelligent soft-sensor model for
dynamic particle size estimation in grinding circuits, International
Symposium on Neural Networks, Advances in Neural Networks –ISNN 2005, 3498 (2005) 871–876. DOI: 10.1007/11427469_138
Toorajipour R., Sohrabpour V., Nazarpour A., Oghazi P., Fischl M., Artificial intelligence in supply chain management: a systematic literature
review, Journal of Business Research, 122 (2021) 502–517. DOI:
10.1016/j.jbusres.2020.09.009
Tsai C.C., Lu C.H., Fuzzy supervisory predictive PID control of a plastics
extruder barrel, Journal of the Chinese Institute of Engineers, 21(1998) 619–624. DOI: 10.1080/02533839.1998.9670423
Udrescu S.-M., Tan A., Feng J., Neto O., Wu T., Tegmark M., AI Feynman
2.0: pareto-optimal symbolic regression exploiting graph modularity,
Advances in Neural Information Processing Systems, 33 (2020)
12pp.
Uhlemann J., Diedam H., Hoheisel W., Schikarski T., Peukert W., Modeling and simulation of process technology for nanoparticulate drug
formulations—a particle technology perspective, Pharmaceutics, 13(2021) 22. DOI: 10.3390/pharmaceutics13010022
Venkatasubramanian V., The promise of artificial intelligence in chemicalengineering: is it here, finally?, AIChE Journal, 65 (2019) 466–478.
DOI: 10.1002/aic.16489
Wahl C.B., Aykol M., Swisher J.H., Montoya J.H., Suram S.K., Mirkin
C.A., Machine learning-accelerated design and synthesis of polyelemental heterostructures, Science Advances, 7 (2021) eabj5505. DOI:
10.1126/sciadv.abj5505
Wang T., Ramik D.M., Sabourin C., Madani K., Intelligent systems
for industrial robotics: application in logistic field, Industrial
Robot: An International Journal, 39 (2012) 251–259. DOI:
10.1108/01439911211217071
Wilts H., Garcia B.R., Garlito R.G., Gómez L.S., Prieto E.G., Artificial
intelligence in the sorting of municipal waste as an enabler ofthe circular economy, Resources, 10 (2021) 28. DOI: 10.3390/
resources10040028
Wisniewski J., Optimal design of reinforcing fibres in multilayer composites using genetic algorithms, Fibres And Textiles In Eastern Europe,
12 (2004) 58–63.
Ying X., An overview of overfitting and its solutions, Journal of Physics:
Conference Series, 1168 (2019) 022022. DOI: 10.1088/17426596/1168/2/022022
Zhang S., Wang F., He D., Jia R., Batch-to-batch control of particle size
distribution in cobalt oxalate synthesis process based on hybrid
model, Powder Technology, 224 (2012) 253–259. DOI: 10.1016/j.
powtec.2012.03.001
Zhang X., Chan F.T.S., Adamatzky A., Mahadevan S., Yang H., Zhang Z.,
Deng Y., An intelligent physarum solver for supply chain network
design under profit maximization and oligopolistic competition,
International Journal of Production Research, 55 (2017) 244–263.
DOI: 10.1080/00207543.2016.1203075
Zhu M., Chen X., Zhou C.-s., Xu J.-s., Musa O., Numerical study of
micron-scale aluminum particle combustion in an afterburnerusing two-way coupling CFD–DEM approach, Flow, Turbulenceand Combustion, 105 (2020) 191–212. DOI: 10.1007/s10494-01900104-1

Christoph Thon et al. / KONA Powder and Particle Journal No. 41 (2024) 3–25

Review Paper

Authors’ Short Biographies
Christoph Thon
Christoph Thon studied bio- and chemical engineering at the Technische Universität Braunschweig
and the Ostfalia University of Applied Sciences from 2011 to 2017 and finished with his master’s
thesis, “Investigation of the stress distribution through laminar flows by using CFD for scaletransfer of kneading processes”. His doctoral research, under the supervision of Prof. Dr.-Ing.
Carsten Schilde at the Institute for Particle Technology (iPAT), focuses on the investigation of grinding processes in mills through computer simulations and the application of artificial intelligence.

Marvin Röhl
Marvin Röhl studied mechanical engineering at the Technische Universität Braunschweig from2013 to 2020 and graduated with his master’s thesis, “Modeling of a drying and structure formation
process via coupling of DEM simulation and methods of artificial intelligence”. His doctoral thesis,
under the supervision of Prof. Dr.-Ing. Carsten Schilde at the Institute for Particle Technology (iPAT)
deals with the development of an intelligent controlling approach to an electrode coating processusing machine learning methods.

Somayeh Hosseinhashemi
Somayeh Hosseinhashemi studied computational science and engineering at the University of
Rostock from 2017 to 2020 and graduated with her master’s thesis, “Numerical investigation ofpressure ripple in a rotary vane pump to determine optimization potentials with the software
StarCCM+” at Robert Bosch Automotive Steering Company. Her doctoral thesis, under the supervision of Prof. Dr.-Ing. Carsten Schilde at the Institute for Particle Technology (iPAT), addresses thedevelopment of an intelligent and autonomous paste manufacturing process using machine learning
approaches.

Arno Kwade
Prof. Dr.-Ing. Arno Kwade studied mechanical engineering at TU Braunschweig and University ofWaterloo, finishing his PhD in 1996, receiving the Heinrich-Büssing-Price. He is head of the Institutefor Particle Technology, Scientific Director of the Battery Lab Factory, spokesman of the Center of
Pharmaceutical Engineering at TU Braunschweig, division head of the Fraunhofer Project Center
for Energy Storage and Systems, member of the National Academy of Science and Engineering,
chairman of the ProcessNet and EFCE working parties “Comminution and classification”, vice chairman of the Advisory Board Battery Research Germany, and chair of the PARTEC 2024, with 300+
publications, a Google Scholar H Index of 40, and over 6000 citations.

Carsten Schilde
Prof. Dr-Ing. Carsten Schilde studied bioengineering at the Technische Universität Braunschweig
and completed his PhD thesis on “Structure, Mechanics and Fracture of Nanoparticulate Aggregates” under Prof. Dr.-Ing. Arno Kwade. Later, he worked at the Institute for Particle Technology
as a research associate and was appointed Assistant Professor for “Particle Simulation & Functional
Structures” in 2017. In 2019, he received the Friedrich-Löffler award. His research focuses on the investigation of particulate systems and structures using computer simulation and artificial intelligence.
His main application fields are pharmaceuticals and composite materials. With over 60 publications,
he has a Google Scholar H Index of 18 and over 1000 citations.

25