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Title: Artificial intelligence for the modeling and control of combustion processes: a review
Authors: Kalogirou, Soteris A. 
Major Field of Science: Engineering and Technology
Field Category: Environmental Engineering
Keywords: Artificial intelligence;Expert systems;Neural networks;Genetic algorithms;Fuzzy logic;Combustion;Internal combustion engines
Issue Date: 2003
Source: Progress in Energy and Combustion Science, 2003, vol. 29, no. 6, pp. 515-566
Volume: 29
Issue: 6
Start page: 515
End page: 566
Journal: Progress in Energy and Combustion Science 
Abstract: Artificial intelligence (AI) systems are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with non-linear problems, and once trained can perform prediction and generalization at high speed. They have been used in diverse applications in control, robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimization, signal processing, and social/psychological sciences. They are particularly useful in system modeling such as in implementing complex mappings and system identification. AI systems comprise areas like, expert systems, artificial neural networks, genetic algorithms, fuzzy logic and various hybrid systems, which combine two or more techniques. The major objective of this paper is to illustrate how AI techniques might play an important role in modeling and prediction of the performance and control of combustion process. The paper outlines an understanding of how AI systems operate by way of presenting a number of problems in the different disciplines of combustion engineering. The various applications of AI are presented in a thematic rather than a chronological or any other order. Problems presented include two main areas: combustion systems and internal combustion (IC) engines. Combustion systems include boilers, furnaces and incinerators modeling and emissions prediction, whereas, IC engines include diesel and spark ignition engines and gas engines modeling and control. Results presented in this paper, are testimony to the potential of AI as a design tool in many areas of combustion engineering.
ISSN: 03601285
DOI: 10.1016/S0360-1285(03)00058-3
Rights: © Elsevier
Type: Article
Affiliation : Higher Technical Institute Cyprus 
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