Neural networks in designing fuzzy systems for real world applications
Fuzzy Sets and Systems
The neural network model RuleNet and its application to mobile robot navigation
Fuzzy Sets and Systems - Special issue on methods for data analysis in classificatin and control
Expert Systems with Applications: An International Journal
Analysis of data for the carbon dioxide capture domain
Engineering Applications of Artificial Intelligence
Learning and tuning fuzzy logic controllers through reinforcements
IEEE Transactions on Neural Networks
On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm
IEEE Transactions on Neural Networks
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The objective of this paper is to study the relationships among the significant parameters impacting carbon dioxide (CO"2) production. An enhanced understanding of the intricate relationships among the process parameters enables prediction and optimization, thereby improving efficiency of the CO"2 capture process. Our work adopted a fuzzy logic approach that explores the relationships among the parameters involved in the amine-based post combustion CO"2 capture process at the International Test Centre for CO"2 Capture (ITC) located in Regina, Saskatchewan of Canada. The key process parameters were selected based on a review of relevant literature and interviews with experts. The adaptive-network-based fuzzy inference system (ANFIS) technique was trained with historical data and generated the membership functions and rules which best interpret the input/output relationships in the process. Four fuzzy inference systems were independently developed for four output parameters, each of which consists of four inputs and 144 rules. The model validation process showed that modeling accuracies of these fuzzy inference systems are within acceptable limits. The developed fuzzy inference systems constitute a knowledge base on the parameters involved in the CO"2 capture process, and can be further expanded and improved for prediction and optimization of the CO"2 capture process in the future.