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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: © 2015 |Pages: 32
ISBN13: 9781466672581|ISBN10: 1466672587|EISBN13: 9781466672598
DOI: 10.4018/978-1-4666-7258-1.ch014
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MLA

Cai, Tianxing. "Application of Artificial Intelligence Techniques to Handle the Uncertainty in the Chemical Process for Environmental Protection." Handbook of Research on Artificial Intelligence Techniques and Algorithms, edited by Pandian Vasant, IGI Global, 2015, pp. 446-477. https://doi.org/10.4018/978-1-4666-7258-1.ch014

APA

Cai, T. (2015). Application of Artificial Intelligence Techniques to Handle the Uncertainty in the Chemical Process for Environmental Protection. In P. Vasant (Ed.), Handbook of Research on Artificial Intelligence Techniques and Algorithms (pp. 446-477). IGI Global. https://doi.org/10.4018/978-1-4666-7258-1.ch014

Chicago

Cai, Tianxing. "Application of Artificial Intelligence Techniques to Handle the Uncertainty in the Chemical Process for Environmental Protection." In Handbook of Research on Artificial Intelligence Techniques and Algorithms, edited by Pandian Vasant, 446-477. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-7258-1.ch014

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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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