Abstract
Modern data analytics techniques and computationally inexpensive software tools are fueling the commercial applications of data-driven decision making and process optimization strategies for complex industrial operations. In this paper, modern and reliable process modeling techniques, i.e., multiple linear regression (MLR), artificial neural network (ANN), and least square support vector machine (LSSVM), are employed and comprehensively compared as reliable and robust process models for the generator power of a 660 MWe supercritical coal combustion power plant. Based on the external validation test conducted by the unseen operation data, LSSVM has outperformed the MLR and ANN models to predict the power plant’s generator power. Later, the LSSVM model is used for the failure mode recovery and a very successful operation control excellence tool. Moreover, by adjusting the thermo-electric operating parameters, the generator power on an average is increased by 1.74%, 1.80%, and 1.0 at 50% generation capacity, 75% generation capacity, and 100% generation capacity of the power plant, respectively. The process modeling based on process data and data-driven process optimization strategy building for improved process control is an actual realization of industry 4.0 in the industrial applications.
Highlights
Given the increased domestic and commercial industrial sectors, almost 100% of energy consumption has increased over the last four decades [1]
The best response performing andgenerator reliable process is utilized for twoof objectives, to plot the characteristics of the power model under the failure mode principal objectives, i.e., (1) to plot the characteristics response of the generator power under the the power plant; and (2) to optimize the generator power of the supercritical power plant for effective failure mode of the power plant; and (2) to optimize the generator power of the supercritical power control of thermo-electric operating parameters
least square support vector machine (LSSVM) training is based on the structural risk minimization principle (SRM), and its basic concept is to transform the data into a high-dimensional feature space and solve the nonlinear problems in a linear pattern [42]
Summary
Given the increased domestic and commercial industrial sectors, almost 100% of energy consumption has increased over the last four decades [1]. The power generation from large commercial power plants is a highly complex and critical industrial operation. Various process modeling techniques, i.e., MLR, ANN, and LSSVM, are utilized and comprehensively compared for the operational analysis of the generator power production from a 660 MWe supercritical coal power plant. The best response performing andgenerator reliable process is utilized for twoof objectives, to plot the characteristics of the power model under the failure mode principal objectives, i.e., (1) to plot the characteristics response of the generator power under the the power plant; and (2) to optimize the generator power of the supercritical power plant for effective failure mode of the power plant; and (2) to optimize the generator power of the supercritical power control of thermo-electric operating parameters.
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