# Application of Organic-Inorganic Hybrids in Chemical Analysis, Bio- and Environmental Monitoring
## 5 Using Computational Intelligence and Mathematical Simulation and Modeling in OIH  
Driven by the desire of creating a robust and systematic approach and/or platform toward control and optimization of operating conditions and chemical processes themselves, mathematical models were successfully integrated into the chemical industry and became a crucial component at the process development stage. The main purpose of using such models is by means of built-in numerical relationships between system input and output parameters to provide the required and relevant information on the dynamic evolution of the variables of interest as a function of time and space. For bio-based processes such system state variables include the main components of biocatalytic or fermentation reactions (e.g., analyte concentration, biomass growth, etc.) combined with specific bioreactor performance metrics that require the introduction of physical (e.g., feed rate, temperature), chemical (e.g., pH, dissolved oxygen), biochemical (e.g., cells composition), and micro-biological (e.g., contamination) parameters [114]. It is also important to mention that the current research objective is based on promoting efficient measurement, monitoring, modeling, and control (M3C) strategies that were set by the European Federation of Biotechnology (EFB) and the European Society of Biochemical Engineering Science (ESBES). The progress and principles of automated measurement, monitoring, control, and optimization, as well as modeling and quality by design (QbD) techniques, currently applied in biotechnology were carefully reviewed in [114]. Development of models of bio-based systems or processes includes data-driven (black-box or empirical models), mechanistic (white-box or first-principles models), and a combination (grey-box models) of both approaches. Thus, when building up the correlations between system parameters  
Figure 12: The workflow of the methodology for validation of OIH/OInH and biosensors (original scheme). (input-output relations) with minimized process knowledge, empirical models can be applied and be rather helpful for example for screening the behavior of OIH-based biosensors with different analytes. However, such models lack the versatility and possibility of achieving a fundamental understanding that is hidden behind the parameter correlations because of the dependency of empirical models on the specific dataset used at the model development stage. On the contrary, mechanistic models are based on recreating (partly) the fundamental principles behind the behavior of the system, and can be (re)structured according to the final purpose (e.g., process control, experimental data analysis, etc.). Since enzymes are frequently immobilized in biosensor applications, mathematical modelling of biocatalytic processes deserves most attention here. Therefore, depending on the aim and complexity of the enzymatic biosensor model it can be defined as catalyst, reaction, reactor, and/or process model. The catalyst model is a complex mathematical formulation (at molecular level) of the enzyme-substrate interactions, as well as the enzyme sensitivity, stability, and overall performance variation at different experimental conditions. On the other hand, the complete system behavior including kinetics, heat, and mass transfer, physical (i.e., enzyme adsorption) and chemical reaction mechanisms can be reproduced and analyzed by means of the reaction, reactor, and process models [115]. Depending on the task and final purpose of the model, the user can either decide on creating an in-house built model with the possibility to adjust model complexity or can select models incorporated in commercially available software (e.g., DigiSim). As a consequence, in the past years various types of mathematical approaches and models were proposed, developed, and applied to study or reproduce the behavior of individual biosensor designs [116], OIH material interfaces [117], biocatalytic [118; 119], and electrochemical processes [120].