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 (Lamar University, USA)
DOI: 10.4018/978-1-4666-7258-1.ch014
OnDemand PDF Download:
No Current Special Offers


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.
Chapter Preview


Biologically inspired techniques or biologically inspired algorithms is a category of algorithms that imitate the way nature performs. This category has been quite popular, since numerous problems can be solved without rigorous mathematical approaches. They have included the methodologies of artificial neural networks (ANN), genetic algorithms (GA), evolutionary algorithms (EA), particle swarm optimization (PSO), ant colony optimization (ACO), fuzzy logic (FL) and the other methods. This chapter aims to provide their potential application in the industrial and environmental research. Actually we will always involve the study of the cause-effect relationship between the emission and the surrounding environment. With the collection and representation of information in a range of ways, software tools have been created to manage and store this data. This data management enables more efficient searching ability of various types of electronic and digitized information. Various technologies have made the work of research more efficient. Biological inspired data mining techniques have been intensively used in different data mining applications such as data clustering, classification, association rules mining, sequential pattern mining, outlier detection, feature selection, and information extraction in healthcare and bioinformatics. The results of the qualitative or mixed methods research may be integrated to reach the research target. Right now, a lot of software tools are available for the analysis to identify patterns and represent new meanings. The programs extend the capabilities of the researcher in terms of information coding and meaning-making. Machine-enhanced analytics has enabled the identification of aspects of interest such as correlations and anomalies from large datasets. Industrial operations always need large amounts of chemicals and fuels in the processing of manufacturing. It has high risks to originate air emission events. Based on an available air-quality monitoring network, the data integration technologies will be applied to identify the scenarios of the possible emission source and their impact to the environment, so as to timely and effectively support diagnostic and prognostic decisions. In this chapter, the application of biologically inspired techniques for such applications have been developed according to the real application purpose. They will have the capability to identify the potential emission profile and spatial-temporal characterization of pollutant dispersion for a specific region, including reversely estimation of the air quality issues. It provides valuable information for accidental investigations and root cause analysis for an emission event; meanwhile, it helps evaluate the regional air quality impact caused by such an emission event as well. Case studies are employed to demonstrate the efficacy of the developed methodology.

Key Terms in this Chapter

Fuzzy Logic System: Fuzzy logic is a logic form of multiple values. It helps to handle the problems which are not fixed and exact. It may have a truth value that ranges in degree between 0 and 1.

Genetic Algorithms: In the field of artificial intelligence, a genetic algorithm (GA) is a search heuristic with the process of natural selection.

Ant Colony Optimization: The ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs.

Clonal Selection Algorithm: A class of algorithms inspired by the clonal selection theory of acquired immunity that explains how B and T lymphocytes improve their response to antigens over time called affinity maturation.

Immune Network Algorithms: Algorithms inspired by the idiotypic network theory proposed by Niels Kaj Jerne that describes the regulation of the immune system by anti-idiotypic antibodies . Immune network algorithms have been used in clustering, data visualization, control, and optimization domains, and share properties with artificial neural networks.

Artificial Neural Networks: Artificial neural networks are models are derived from animal central nervous systems and can be regarded as systems of internally connected neurons which are capable of machine learning and pattern recognition.

Negative Selection Algorithm: Inspired by the positive and negative selection processes that occur during the maturation of T cells in the thymus called T cell tolerance.

Artificial Immune System: Artificial immune systems (AIS) are a class of computationally intelligent systems inspired by the principles and processes of the vertebrate immune system.

Particle Swarm Optimization: Particle swarm optimization (PSO) helps to optimize a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.

Complete Chapter List

Search this Book: