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Self-adaptive whale optimization for the design and modelling of boiler plant

Savargave S. B., Deshpande A. M.

Control and Cybernetics

2018

Vol. 47, no. 4

329--356

Recently, boiler plants are have been the subject of intensive investigations in the context of energy-saving technologies and management for power saving and reduction of emissions. Modern boiler design oﬀers several beneﬁts with this respect. In the past, improper design of boilers has been the cause of explosions which led to the loss of life and property. Modern designs attempt to avoid such mishaps. This paper presents a novel Self-Adaptive Whale Optimization Algorithm (SAWOA) for improving the learning characteristic of the neural network, the major intention being to model the characteristics of the boiler plant and so to eﬀectively predict the boiler behaviour. The performance analysis of the introduced model has been carried out using the three test cases with consideration of several parameters. In the experimental analysis, the introduced technique is compared with the existing ones, based on such approaches as Neural Model (NM), Fireﬂy (FF-NM), Adaptive Fireﬂy NM (AFF-NM), and Whale Optimization Algorithm-NM (WOANM). In this comparison, the error, i.e. the diﬀerence between the actual and the predicted value, was used, and the results revealed that the error is lower for the introduced technique under diﬀerent experimental scenarios. The experimental results demonstrate that the performance level of SAWOA is by 18% better than those of NM, FF-NM, and AFF-NM, and by 3.74% better than that of WOA-NM. This conﬁrms the quality of performance of the proposed approach regarding boiler plants.