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Application of Artificial Intelligence Techniques to Handle the Uncertainty in the Chemical Process for Environmental Protection

Application of Artificial Intelligence Techniques to Handle the Uncertainty in the Chemical Process for Environmental Protection

Tianxing Cai
Copyright: © 2017 |Pages: 32
ISBN13: 9781522507888|ISBN10: 1522507884|EISBN13: 9781522507895
DOI: 10.4018/978-1-5225-0788-8.ch046
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MLA

Cai, Tianxing. "Application of Artificial Intelligence Techniques to Handle the Uncertainty in the Chemical Process for Environmental Protection." Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2017, pp. 1229-1260. https://doi.org/10.4018/978-1-5225-0788-8.ch046

APA

Cai, T. (2017). Application of Artificial Intelligence Techniques to Handle the Uncertainty in the Chemical Process for Environmental Protection. In I. Management Association (Ed.), Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications (pp. 1229-1260). IGI Global. https://doi.org/10.4018/978-1-5225-0788-8.ch046

Chicago

Cai, Tianxing. "Application of Artificial Intelligence Techniques to Handle the Uncertainty in the Chemical Process for Environmental Protection." In Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1229-1260. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-0788-8.ch046

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Abstract

In the chemical process, the uncertainties are always encountered. Therefore, the algorithm of process modeling, simulation, optimization, and control should have the capability to handle the uncertain parameter. Meta-Heuristics Optimization (MO) techniques are attractive global optimization methods inspired by the various industrial phenomena with uncertainty. These methods have been successfully applied to a wide range of chemical engineering problems with a higher level of degree of satisfaction. In this chapter, the authors introduce multiple artificial intelligence techniques: Genetic Algorithm (GA), Biogeography-Based Optimization (BBO), Differential Evolution (DE), Evolutionary Strategy (ES), Probability-Based Incremental Learning (PBIL), Stud Genetic Algorithm (SGA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Fuzzy Logic (FL). It includes the introduction of algorithms and their applications to handle the uncertainty in the chemical process operation.

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