Insights into the analogy between process optimization and molecular dynamics simulation of the CO2-Monoethanolamine absorption system

Optimization of a thermal manufacturing process: Drawing of optical fibers

CHAPTER TEN - Optimization of seawater desalination systems

Post combustion carbon capture: Does optimization of the processing system based on energy and utility requirements warrant the lowest possible costs?

Business processes of modern companies are characterized by a huge complexity which is caused for example by quickly changing markets, short product life cycles or dynamic interactions between particular subsystems of a company. Business process management is intended to implement efficient and customer orientated processes whereby the simulation of business processes can be used to evaluate the quality of processes and to identify areas of improvements. Since real business processes usually contain decision processes which can be solved by optimization systems, it makes sense to combine the simulation and the optimization of business processes. (Marz et.al. 2010, p 3ff.) As an example of a reasonable combined simulation and optimization of business processes, the navigation in a road network is discussed in this paper. Consider vehicles seeking the fastest route from a starting node to a target node using a navigation system. The amount of time spent driving on an arc is influenced by the distance and the amount of the vehicles on this arc and is continuously changing. The structure of the road network and the traffic within the network is described in a simulation model while the fastest path decisions of each vehicle are made by using an optimization system. There is obviously a relationship between the individual decisions made for each of the vehicles and the state of the entire network. The aim of this paper is to describe how a combined simulation and optimization of business processes can be created through using EPC-Simulator (Muller 2012) as a simulation system and CMPL (Steglich and Schleiff 2010) as an optimization system where the network traffic simulation is used exemplarily. CREATING SIMULATIONS USING EPCSIMULATOR The EPC Simulator is a plugin of the EPC modelling toolbox bflow* (Kern et al. 2010). As shown in Figure 1 the first step to create a simulation model is the specification of a model document and several process documents in bflow*. The model document contains information about the model infrastructure (simulation time, available resources, inter-arrival times of entities, etc.). The process documents contain the process descriptions in EPC notation. Based on these documents, the EPC-Simulator can generate a simulation model. This is a Java Application that uses the DESMO-J Framework, whereby DESMO-J provides the basic functionality of a simulation. (Page and Kreutzer 2005) Figure 1: EPC-Simulator and its environment The Java source code generated by EPC-Simulator contains marked areas where the source code can be extended manually by individual functionalities in functions or decision rules. In this way it is possible to integrate CMPL easily by a code extension through

Abstract: This book, Mastering System Simulation and Optimization with MATLAB Simulink, provides a comprehensive and methodologically grounded exploration of dynamic system modeling, simulation, and optimization using MATLAB Simulink. Positioned at the intersection of system theory, computational modeling, and control engineering, the text constructs a robust conceptual framework for representing and analyzing both linear and nonlinear systems across diverse engineering domains. The book addresses a core challenge in modern engineering: developing accurate and efficient models that enable informed design, control, and decision-making in complex and dynamic environments. The methodology integrates block-diagram modeling, numerical solver strategies, parameter sensitivity analysis, and optimization workflows. Emphasis is placed on both deterministic and metaheuristic optimization techniques, including PID tuning, model predictive control, and genetic algorithms, applied to continuous, discrete, and hybrid systems. Advanced chapters extend the discussion to nonlinear dynamics, event-driven systems, fault modeling, and physical simulation using Simscape and Stateflow. Model verification, validation, and real-time deployment are treated as integral to engineering integrity and industrial applicability. The key outcomes highlight Simulink’s capacity to unify system representation, control design, and optimization into a scalable, interdisciplinary platform. Through detailed case studies, the book illustrates its impact across aerospace, automotive, energy, and biomedical applications. The work serves as both a technical reference and a methodological guide for graduate students, researchers, and engineers seeking to apply rigorous simulation and optimization techniques to real-world system design. Keywords System simulation, MATLAB Simulink, dynamic systems, model-based design, control systems, optimization, hybrid systems, nonlinear dynamics, Simscape, Stateflow, real-time deployment, system identification, PID control, model predictive control, verification and validation, engineering simulation, embedded systems, fault modeling, solver strategies, simulation optimization

Design of experiments in the optimization of nanoparticle-based drug delivery systems.

Process and Integration Optimization of Post-Combustion CO2 Capture System in a Coal Power Plant

Modelling and simulation of complex mechanical systems involving multi-simulation environment, with great accuracy is essential as part of the conceptual design phase of a system under design. The co-simulation between system level model with various subsystems based on Model Based Design (MBD) and component level model is a perfect example. These mathematical models have distinct features such as the type of differential equations used to represent its physics, boundary conditions etc. In this study we use MBD methodology to develop a simulation model that can drive product development and optimization of mechanical systems. We also incorporate a 3-D Computational Fluid Dynamics (CFD) model as one of its subsystem into the System Level (SL) model architecture. This is done by creating a co-simulation interface between these two different modelling environments and exchange boundary conditions via a specific interface. The interaction between multi-domain models through system simulation together with the detailed fluid flow and thermal analysis of the component through CFD is finally explained. Finally, the dynamic behaviour of the complete system and its temporal and spatial scales is shown to be captured in more detail.

The large scale cryogenic systems are complex industrial processes with large number of correlated variables on wide operation ranges. A dynamic simulator is required to obtain the virtual commissioning for the cryogenic plants, which provides a computer aided design platform for developing and testing new control strategies and control programs. This paper presents a cryogenic simulator based on a commercial modeling and simulation software EcosimPro and an open source control system architecture EPICS (Experimental Physics and Industrial Control System), and the communication and control architecture will be explained in detail. The dynamic simulation for a helium cryogenic system during a complete cool-down process has been implemented. The results show that the scheme is feasible to apply the simulation system modeled with EcosimPro to the cryogenic system based on EPICS. The simulation architecture will be used at the Institute of Plasma Physics, Chinese Academy of Sciences (ASIPP) for process and control optimization or operator training for new cryogenic system in the future.

The main aim of this work is to show that such a powerful optimizing tool like evolutionary algorithms (EAs) can be in reality used for the simulation and optimization of a nonlinear system. A nonlinear mathematical model is required to describe the dynamic behaviour of batch process; this justifies the use of evolutionary method of the EAs to deal with this process. Four algorithms from the field of artificial intelligent—differential evolution (DE), self‐organizing migrating algorithm (SOMA), genetic algorithm (GA), and simulated annealing (SA)—are used in this investigation. The results show that EAs are used successfully in the process optimization.

In current scenario, people tend to move towards outskirts and like to settle in places that are close to nature. But, due to urban lifestyle and to fulfill the basic needs, demand of electricity remains the same as in urban areas. This demand of electricity can be only fulfilled by using hybrid renewable energy resources, which is easily available in outskirts. Renewable energy resources are unreliable and more expensive. Researchers are working to make, it more reliable and economic in terms of utilization. This article proposes a metaheuristic grasshopper optimization algorithm (GOA) for the optimal sizing of hybrid PV/wind/battery energy system located in remote areas. The proposed algorithm finds the optimal sizing and configuration of remote village load demand that includes house electricity and agriculture. The optimization problem is solved by minimization of total system cost at a desirable level of loss of power supply’s reliability index (LPSRI). The results of GOA are compared with particle swarm optimization (PSO), genetic algorithm (GA) and hybrid optimization of multiple energy resources (HOMER) software. In addition, results are also validated by modeling and simulation of the hybrid energy system and its configurations at different weather conditions-based results. Hybrid PV/wind/battery is found as an optimal system at remote areas and sizing are[Formula: see text] with cost of energy (COE) (0.3473$/kWh) and loss of power supplies reliability index (LPSRI) (0%). It is clear from the results that GOA based methods are more efficient for selection of optimal energy system configuration as compared to others algorithms.

Modeling and optimization of a fluidized catalytic cracking process under full and partial combustion modes

Alexander G. Madera - Professor, Department of Mathematics of the Faculty of Economics, National Research University Higher School of EconomicsAddress: 20, Myasnitskaya Street, Moscow, 101000, Russian FederationE-mail: amadera@hse.ru This paper is devoted to mathematical modeling and optimization of business processes and process systems under conditions of uncertainty. At present, modeling of business processes is mainly descriptive, which does not allow quantitative modeling and optimization in the design of processes and process systems. In addition, the existing methods of decision-making in business processes are based on the assumption that the decisive factors are deterministic. Despite uncertainty of the real processes caused by the uncertainty of future costs of resources, the market environment, economy, finances, etc,, the factors of an uncertain future are either not taken into account, or are believed to be the same as those observed currently. In this paper, a stochastic interval mathematical optimization model is developed. This model allows us to simulate in a quantitative way the business processes and process systems in which they take place, taking into account the uncertainties of the future state of the economy, finances, market environment, costs of resources, as well as future realization of chances and risks related to the productive, supporting, and service processes. The criterion for optimality of the model is the maximization of the smallest deviation of the projected chances and risks, which makes it possible to make the best decision in the case that the most unfavorable conditions for the business process occur in the future. The criterion of optimality adopted in the mathematical model takes into account not only the uncertainty of the future state of the economy, finance, and market environment, but also the psychology of decision-making and the subjective nature of judgments and estimates. We present a concept and method for estimating the inductive (logical, subjective) probabilities of the occurrence of uncertain predicted business process factors. The models and methods developed in the paper make it possible to carry out mathematical modeling and optimization of business processes in a variety of activities without restrictions on the complexity of the structural model of the business process, the qualitative and quantitative composition of the connections in the process systems. On their basis, a software package for the quantitative design of business processes and process systems under conditions of uncertainty can be developed.

Abstract. Self-Propelled Sugar Beet harvesters are complex machines. To improve system performance and support operators the development of an assistance system for process optimization was investigated. This article summarizes the results of the application of modern machine learning tools and states out a development approach which integrates them into the development process. Starting from a harvesting process description, a quality sensor gets integrated into the machine, the algorithm concept for the sensor evaluation is shown, as well as the machine learning-based process optimization and first qualitative evaluation of the overall system are presented.

Process optimization and scale-up of a rapid thermal processing system was done for TiSi2 films using design of experiments. The conventional two-step TiSi2 process was studied and an experimental design conducted with ten process variables. The screening experiment used to test process variables for main effects, indicated that only three of the ten process variables—low temperature anneal, time of anneal, and Ti film thickness—affected the measured film properties with greater than 95% significance. A second D-optimal design experiment was used to study the affect of three process variables on the film properties of TiSi2. This design studied the main effects as well as quadratic interactions of the process variables on the measured responses of the TiSi2 film. The experimental results predicted an optimized TiSi2 film with 0.60% uniformity and 0.96 Ω/sq sheet resistance. These predicted results were confirmed by device lots. The process capability index of the transferred process is 3.12.