# Computer-Aided Molecular Design for Bio-Based Solvent Selection from Citrus and Coffee Wastes for Furfural Extraction

Giovana C.A. Netto, Moisés Teles dos Santos, Vincent Gerbaud

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Giovana C.A. Netto, Moisés Teles dos Santos, Vincent Gerbaud. Computer-Aided Molecular Design for Bio-Based Solvent Selection from Citrus and Coffee Wastes for Furfural Extraction. ESCAPE 35 35th European Symposium of Computer Aided Process Engineering, Jan Van Impe, Grégoire Léonard, Satyajeet Sheetal Bhonsale, Monika Polanska, Filip Logist, Jul 2025, Ghent, Belgium. pp.2429-2434, 10.69997/sct.167691. hal-05348932

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# Computer-Aided Molecular Design for Bio-Based Solvent Selection from Citrus and Coffee Wastes for Furfural Extraction

Giovana C. A. Netto<sup>a*</sup>, Moisés Teles dos Santos, and Vincent Gerbaud<sup>b</sup>

## ABSTRACT

The global reliance on fossil-based solvents, such as toluene, in industrial processes has spurred efforts to identify sustainable alternatives. This study employs the IBSS® CAMD tool to evaluate building blocks derived, directly or indirectly, from agricultural residues - specifically orange and coffee wastes-, to replace toluene for furfural extraction. Using multi-objective optimisation and group contribution methods, millions of molecules were analysed, and the research identifies promising bio-based solvent candidates for furfural extraction. These candidates demonstrated superior performance, with improved Hansen Solubility Parameter (HSP) proximity to furfural, favourable melting and boiling points, and low bioaccumulation potential. This study underscores the potential of valorizing Brazil's abundant coffee and orange residues, which remain largely underutilized despite their rich composition of bioactive compounds. The findings highlight the dual benefits of addressing agricultural waste management challenges while advancing sustainable industrial practices. While the results are encouraging, further validation of the stability and scalability of these molecules is necessary to ensure their practical implementation.

**Keywords:** Biomass, Genetic Algorithm, Toluene, Environment, Food & Agricultural Processes, CAMD, Furfural, Solvent, Agricultural Wastes

## INTRODUCTION

Brazil has an outstanding agricultural production that reaches 94 million ha [2], which represents 11% of the nation's total territory. The country stands as the world's leading producer of several key commodities, including coffee and oranges. However, alongside such intensive production comes an increasing concern about the sustainable management of the vast amounts of agricultural waste generated throughout the supply chain [3].

In 2023, Brazil's coffee production reached 3.5 million tons, primarily through the dry processing method, where coffee cherries are dried immediately after harvest [5]. Notably, 90-99% of the fruit mass is classified as waste [1]. Also, in 2023, Brazil produced 18 million ton of orange, of which 72% [15] was destined for orange juice production. During this process, 50-60% of

the fruit mass becomes waste, primarily composed of seeds and peel [14].

Agricultural wastes from coffee and orange production are typically disposed of in the fields, repurposed as animal feed or incinerated [14]. These practices contribute to environmental impacts and these approaches overlook the significant potential of these residues. Rich in bioactive compounds, such as antioxidants and fibers, coffee and orange byproducts offer untapped opportunities for sustainable valorization [3].

The residues contain many building blocks that can be extracted or produced from them and one that stands out is furfural. Furfural is produced via the dehydration of xylose, a sugar present in the hemicellulose fraction of lignocellulosic biomass. It has a wide range of industrial applications, including the production of plastics, pharmaceuticals, agrochemical products, non-petroleum-derived chemicals and is a precursor for

<footnote><sup>a</sup> University of São Paulo (USP), Department of Chemical Engineering, São Paulo, São Paulo, Brazil</footnote>

<footnote><sup>b</sup> Université de Toulouse, INP, UPS, Laboratoire de Génie Chimique (LGC), Toulouse, France</footnote>

<footnote>* Corresponding Author: giovana.netto@usp.br.</footnote>
various furan-based chemicals and feedstocks [10].

Although the synthesis of furfural dates to 1921, the process still faces challenges, including low efficiency (~50%) and high energy demands, especially when distillation is employed as a purification method. Therefore, LLE appears as a possible purification method and the main solvent used is toluene, a fossil-based toxic solvent [13].

The procedure for solvent selection plays a crucial role in determining the efficiency and viability of the proposed new solvent. In this study, Computer Aided Molecular Design (CAMD) was chosen as an effective early-stage tool for discovering new molecules, which leverages the simplicity of semi-empirical quantitative structure-property relationships (QSRs) in conjunction with genetic optimization algorithms [5].

An in-house CAMD tool called IBSS® was employed. This tool was developed in compliance with a model driven software engineering approach, and it is based on different analysis models (UML and BPMN) and object-oriented implementation's models dedicated to Microsoft.NET framework (C# and VB.NET) [7]. IBSS® has been successfully applied in various contexts, such as identifying solvents for the perfume industry [12], developing greener solvents for photovoltaic panel production [16], and substituting hexane in metabolite extraction from biomass feedstock [11].

The objective of this work was to evaluate solvents derived from coffee and orange residues, to extract furfural from an aqueous solution with methanol and acetic acid, in substitution to toluene.

## METHODOLOGY AND PROBLEM FORMULATION

To accomplish the goal of finding a suitable bio-based solvent to replace toluene for furfural extraction, a three-stage methodology, depicted in Figure 1, was proposed based on the work of Heintz et al. [6].

<figure><img src="image_2.png" /><figcaption>**Figure 1:** Methodology for solvent selection using IBSS® tool.</figcaption></figure>

## Literature Review

The first stage entailed a comprehensive review of the literature to identify potential building blocks derived, directly or indirectly, from coffee and orange residues. From an initial pool of 46 building block families, 13 were selected for further evaluation: furoate, geranyl, glucaric acid, glutamic acid, hydroxymethylfurfural, hydroxypropionic acid, levulinic acid, limonene, 5-methylfurfural, oleic acid, succinic acid, glycerol, and furfural itself. Considering the derivatives from the abovementioned building block families, 103 different fixed fragments were analysed.

### IBSS® Optimisation

The second stage corresponds to the IBSS® optimisation, what involves essentially six key components: (1) building block library construction, (2) molecular representation, (3) properties setup, (4) properties calculation, (5) performance criteria for molecule evaluation, and (6) optimisation [6]. The output consisted of a .txt file generated for the 200th generation of each fixed fragment studied, containing the properties, performance metrics, and molecular graph structures of the individuals ranked based on their calculated performance.

### Building block library construction

IBSS® is a flexible tool, and it is possible to add new building blocks to be tested. After the literature review, a preliminary verification was conducted to determine whether the molecules selected were already present in the IBSS® library. For those not included, new entries were created by assigning an ID, constructing the molecular graph, and defining possible functionalization points.

The newly fixed building blocks were drawn, and their SMILES representation were determined, which was entered in "evaluation mode", in the tool, to determine the molecular graph.

This process resulted in the creation of 7 new families of building blocks, comprising a total of 39 new fixed building blocks molecules.

### Molecular representation

The selected molecular representation is molecular graph. In this representation, the diagonal elements represent non-hydrogen atoms, and the off-diagonal elements represent chemical connections. One of the implemented molecules is represented in Figure 2 as an example.
<figure><img src="image_3.png" /><figcaption>Figure 2: Molecular representation of Limonene</figcaption></figure>

## Properties set up

The IBSS® tool allows users to specify the properties desired to calculate performance, assigning their weights, target values, tolerances, and value of performance in the tolerance value.

The target properties chosen, detailed in Table 1, were defined based on the recommendations of Heintz et al. [6].

The most critical property is the Hansen Solubility Parameter (HSP) distance, which accounts for three key interaction forces: D (London dispersion forces), P (dipole-dipole polarity interactions), and H (hydrogen bonding). The strategy was to minimize the HSP distance of the candidate molecules from furfural. To reflect practical considerations, the HSP distance between toluene and furfural (13.9) was set as the tolerance value, given that toluene is a viable solvent for furfural extraction.

For each configuration of fixed-building blocks and kmax (maximum number of fragments added in the fixed building block structure, varied from 4 to 8), a different input XML file was created. These files included the target properties (Table 1) and the variable fragments (-CH2-, -CH-- , --C-- , CH2=, -CH=, --C=, CH=, -C=, NH2-, -NH-, -N-- , NH=, N=-, OH-, -O-, O=).

## Properties calculation

Using group contribution methods for property estimation, the tool is able to predict the melting point, boiling point, log(kow) and HSP distance with models developed by Hukkerikar et al. [8] and flash point with a model created by Catoire, Paulmier and Naudet [4].

## Performance criteria for molecule evaluation

The performance of each candidate molecule is calculated from a performance function (eq. 1) and it considers deviation of predicted property values from the target values (eq. 2).

$$ Perf(MG_i) = \frac{\sum_{p=1}^{np} w_p \cdot F_p(MG_i)}{\sum_{p=1}^{np} w_p} \quad (1) $$

$$ F_p(MG_i) = \exp\left[\ln(val) \cdot \left(\frac{P-x}{Tol}\right)^2\right] \quad (2) $$

Where $Mg_i$ is the molecular graph, $Perf$ is the calculated performance, $n_p$ is the number of target properties, $F_p$ is the performance function, $w_p$ is the weight of performance $p$, $Tol$ is the tolerance set, $x$ is the target value for property and $P$ is the property value.

## Optimisation

The problem solved by IBSS® is a multi-objective optimization, since multiple properties are considered in the performance equation. A genetic algorithm is used, which modifies the molecular graphs thought mutation, crossover, deletion and insertion. Newly generated molecules have their performance calculated and are ranked accordingly. The molecules with the best performance are selected for the next generation. All the parameters of the genetic algorithm presented in Table 2 were used in this study, but IBSS® also permits their modification.

<table><caption>Table 1: Target properties setup.</caption><thead><tr><th>Stream</th><th>Weight</th><th>Target Value</th><th>Tolerance</th><th>Corresponding Performance</th><th>Justification</th><th>Reference</th></tr></thead><tbody><tr><td>HSP Distance</td><td>3</td><td>0</td><td>13,9</td><td>0,9</td><td>Good solubility</td><td>[8]</td></tr><tr><td>Melting Point</td><td>1</td><td>&lt; 10°C</td><td>10</td><td>0,8</td><td>Liquid at usage temperature</td><td>[8]</td></tr><tr><td>Boling Point</td><td>1</td><td>&gt;250°C</td><td>3</td><td>0,8</td><td>Liquid at usage temperature</td><td>[8]</td></tr><tr><td>Flash Point</td><td>1</td><td>&gt;61°C</td><td>3</td><td>0,8</td><td>Non flammable</td><td>[4]</td></tr><tr><td>Log (Kow)</td><td>1</td><td>&lt;3</td><td>0,8</td><td>0,8</td><td>Low potential of bioaccumulation</td><td>[8]</td></tr></tbody></table>
<table><caption>**Table 2:** Parameters of the genetic algorithm.</caption><thead><tr><td>Property</td><td>Value</td></tr></thead><tbody><tr><td>Elitism</td><td>5</td></tr><tr><td>Population size</td><td>100</td></tr><tr><td>Number of generations</td><td>200</td></tr><tr><td>Crossover probability</td><td>65%</td></tr><tr><td>Mutation probability</td><td>15%</td></tr><tr><td>Insertion probability</td><td>10%</td></tr><tr><td>Deletion probability</td><td>10%</td></tr></tbody></table>

## Results Analysis

The third stage involved an evaluation of the results to identify the most promising candidates. A total of 16 thousand molecules were generated in the last generations and the main results are presented below.

## RESULTS

The computational exploration conducted using the IBSS® CAMD tool enabled the evaluation of millions of potential molecules, leading to the identification of promising alternatives to toluene for liquid-liquid extraction (LLE) of furfural. The best molecules, for each fixed building block, were ranked based on their performance and the outcome is presented in Table 3. The results indicate that derivatives of glycerol, limonene, and furfural—building blocks derived directly or indirectly from orange and coffee wastes—performed best. Nevertheless, 13 configurations exhibited high performances (above 0.9800), a promising outcome that suggests multiple alternative solvents worth exploring.

Analysing the results in Table 3, it is not possible to stablish a direct connection between number of radicals and performance, but it is noticed that for limonene, hydroxypropionic and glutamic acid, the increase in the number of functionalization points was positive to the performance and the opposite behavior was identified for glycerol, hydroxymethylfurfural, methyfurfural, geranyl and succinic acid.

Also, it is observed that the biggest molecules, in general, didn't score high performances. The average of non-hydrogen atoms for the molecules with performances greater than 0,9 was 14, while for the molecules with performances less than 0,9, it was 26.

Three of the best molecules presented in Table 3 were selected and are detailed in Table 4. The values of the properties are highlighted in green if they meet the target value or in blue if they don't meet the exact target but fall within the acceptable range defined by the target plus the tolerance.

<table><caption>**Table 3:** Best molecules generated for each building block.</caption><thead><tr><td>Fixed Building Block</td><td>Kmax</td><td>FP*</td><td>Best Performance</td></tr></thead><tbody><tr><td>Glycerol</td><td>8</td><td>1</td><td>0,9986</td></tr><tr><td>Glycerol</td><td>4</td><td>2</td><td>0,9955</td></tr><tr><td>Limonene</td><td>4</td><td>3</td><td>0,9937</td></tr><tr><td>Furfural</td><td>8</td><td>1</td><td>0,9931</td></tr><tr><td>Furoate</td><td>7</td><td>1</td><td>0,9931</td></tr><tr><td>Limonene</td><td>4</td><td>2</td><td>0,9930</td></tr><tr><td>Hydroxypropionic</td><td>4</td><td>3</td><td>0,9925</td></tr><tr><td>Hydroxymethylfurfural</td><td>4</td><td>1</td><td>0,9922</td></tr><tr><td>Levulinic Acid</td><td>5</td><td>1</td><td>0,9914</td></tr><tr><td>Hydroxypropionic</td><td>4</td><td>2</td><td>0,9913</td></tr><tr><td>Succinic Acid</td><td>6</td><td>2</td><td>0,9904</td></tr><tr><td>Glutamic Acid</td><td>4</td><td>4</td><td>0,9883</td></tr><tr><td>Limonene</td><td>7</td><td>1</td><td>0,9876</td></tr><tr><td>Methylfurfural</td><td>5</td><td>1</td><td>0,9870</td></tr><tr><td>hydroxymethylfurfural</td><td>4</td><td>2</td><td>0,9848</td></tr><tr><td>Geranyl</td><td>8</td><td>1</td><td>0,9843</td></tr><tr><td>Geranyl</td><td>4</td><td>2</td><td>0,9817</td></tr><tr><td>Methylfurfural</td><td>4</td><td>2</td><td>0,9810</td></tr><tr><td>Glycerol</td><td>6</td><td>5</td><td>0,8571</td></tr><tr><td>Glutamic Acid</td><td>5</td><td>2</td><td>0,8571</td></tr><tr><td>Hydroxymethylfurfural</td><td>5</td><td>3</td><td>0,8571</td></tr><tr><td>Glutamic Acid</td><td>7</td><td>1</td><td>0,8568</td></tr><tr><td>Succinic Acid</td><td>8</td><td>1</td><td>0,8568</td></tr><tr><td>Glycerol</td><td>7</td><td>3</td><td>0,8567</td></tr><tr><td>Succinic Acid</td><td>5</td><td>3</td><td>0,8561</td></tr><tr><td>Glucaric Acid</td><td>7</td><td>6</td><td>0,8552</td></tr><tr><td>Oleic Acid</td><td>6</td><td>2</td><td>0,8550</td></tr><tr><td>Glycerol</td><td>6</td><td>6</td><td>0,8548</td></tr><tr><td>Succinic Acid</td><td>7</td><td>4</td><td>0,8541</td></tr><tr><td>Oleic Acid</td><td>7</td><td>1</td><td>0,8510</td></tr><tr><td>Hydroxypropionic</td><td>7</td><td>1</td><td>0,8496</td></tr></tbody></table>

*FP = functionalization point

In all three cases, the Hansen Solubility Parameter (HSP) distances were significantly smaller than the toluene-furfural distance of 13.9. Additionally, their melting and boiling points suggest that these solvents would remain liquid under ambient conditions and based on their flash point they are classified as non-flammable. Furthermore, their low log(Kow) values indicate minimal bioaccumulation concerns, further supporting their suitability as sustainable solvent alternatives.

As shown in Figure 3, the maximum performance of the best molecule, derived from glycerol with one functionalization point, improved as generations progressed. This trend demonstrates the effectiveness of the genetic algorithm in identifying better-performing individuals across the tested generations. Furthermore, the
<table><caption>**Table 2:** Best molecule for each of the best 3 fixed building blocks.</caption><thead><tr><th>Property</th><th>Limonene Derivative</th><th>Furfural Derivative</th><th>Hydroxymethylfurfural Derivative</th></tr></thead><tbody><tr><td>Fixed building block structure</td><td></td><td></td><td></td></tr><tr><td>New molecule structure</td><td></td><td></td><td></td></tr><tr><td>Fixed building block</td><td>Limonene</td><td>Furfural</td><td>Hydroxymethylfurfural</td></tr><tr><td>Number of FP*</td><td>3</td><td>1</td><td>1</td></tr><tr><td>KMax**</td><td>4</td><td>8</td><td>4</td></tr><tr><td>Performance</td><td>0,9937</td><td>0,9931</td><td>0,9922</td></tr><tr><td>Melting point (°C)</td><td>11,95</td><td>10,09</td><td>10,06</td></tr><tr><td>Boiling point (°C)</td><td>304,99</td><td>286,48</td><td>264,69</td></tr><tr><td>Flash point (°C)</td><td>152,96</td><td>148,42</td><td>126,33</td></tr><tr><td>HSP distance (MPa)<sup>1/2</sup></td><td>4,69</td><td>5,46</td><td>5,79</td></tr><tr><td>Log (kow)</td><td>1,92</td><td>0,66</td><td>1,06</td></tr></tbody></table>

*FP: functionalization point

**KMax: maximum number of fragments added in the fixed building block structure

results reveal that as kmax increases, the maximum performance also improves, highlighting the algorithm's enhanced flexibility to create diverse molecular structures with higher potential.

<figure><img src="image_4.png" /><figcaption>**Figure 3:** Methodology for solvent selection using IBSS® tool.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Figure 4:** Molecular representation of molecules generated by IBSS®</figcaption></figure>

From the list in Table 1, some molecules were selected and drawn based on the molecular graph provided as an output of IBSS®. These molecules are depicted in Figure 4. The chemical structures are diverse, and it is important to emphasize that further investigations are needed into process design, stability, and the viability of the proposed molecules. Nevertheless, this study highlights the potential of utilizing residues from high-demand fruits to design environmentally friendly solvents with superior performance characteristics.

## CONCLUSION AND NEXT STEPS

The results demonstrate that the IBSS® CAMD tool is an effective approach for designing bio-based solvents with the desired properties to replace toluene in liquid-liquid extraction (LLE) of furfural from aqueous solutions. Through multi-objective optimisation, alternative solvents were efficiently ranked based on a global performance value. While this early-stage methodology shows significant promise, further studies are required to assess the stability and feasibility of synthesizing these molecules. Nonetheless, the findings highlight a
valuable pathway for developing alternative solvents derived from orange and coffee waste—abundant residues in Brazil—offering a sustainable solution to reduce reliance on fossil-based solvents.

## ACKNOWLEDGEMENTS

This study was financed, in part, by the São Paulo Research Foundation (FAPESP), Brazil. Process Number 2024/03274-9.

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