Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency

Abstract

This paper presents a comprehensive step-wise methodology for implementing industry 4.0 in a functional coal power plant. The overall efficiency of a 660 MWe supercritical coal-fired plant using real operational data is considered in the study. Conventional and advanced AI-based techniques are used to present comprehensive data visualization. Monte-Carlo experimentation on artificial neural network (ANN) and least square support vector machine (LSSVM) process models and interval adjoint significance analysis (IASA) are performed to eliminate insignificant control variables. Effective and validated ANN and LSSVM process models are developed and comprehensively compared. The ANN process model proved to be significantly more effective; especially, in terms of the capacity to be deployed as a robust and reliable AI model for industrial data analysis and decision making. A detailed investigation of efficient power generation is presented under 50%, 75%, and 100% power plant unit load. Up to 7.20%, 6.85%, and 8.60% savings in heat input values are identified at 50%, 75%, and 100% unit load, respectively, without compromising the power plant’s overall thermal efficiency.

A Bibliometric Analysis and Review of Supercritical Fluids for the Synthesis of Nanomaterials

“…Since the discovery of the supercritical state in the 19th century, various studies have been carried out, and it has been applied and popularized in petrochemical and environmental protection [ 1 ], thermal power generation [ 2 ], food and drug industries [ 3 , 4 ], biomedical applications [ 5 , 6 , 7 ], material preparation [ 8 , 9 ], etc. This has gradually led to the formation of supercritical fluid extraction technology, supercritical fluid chromatography technology, supercritical dyeing technology, and supercritical drying technology, etc.…”

Construction of Operational Data-Driven Power Curve of a Generator by Industry 4.0 Data Analytics

“…Energy efficiency is the key element of Industry 4.0, and digital energy transformation goals promise energy savings and process optimization of the power systems. In this regard, the data-driven optimum control strategies and construction of custom-built performance profiles of industrial machinery are the promising paradigm shift for achieving operational excellence and energy efficiency for the energy systems [2][3][4].…”

“…Unlike mathematical modeling which requires detailed process analyses, a few systems' representative operation parameters are needed to construct the AI models [5,11]. The AI models present excellent predictability and are also deployed as the operation excellence tools for industrial operations [3,10]. In this regard, the AI techniques, including artificial neural networks (ANNs) and automated machine learning (AutoML) platforms, are well suited for improving the process operation management, material, energy savings, failure diagnosis, and, ultimately, the efficient industrial practices [12][13][14].…”

“…ANNs' remarkable features make them suitable for industrial operations to be effectively stimulated for enhancing the energyefficient process control of industries. [2,3,7,15,17,18]. Recently, AutoML techniques have been popular in various applications, as they help develop high-quality AI models.…”

“…The useful operating regions of critically controlled parameters have been identified. Moreover, the savings in energy spent are achieved without compromising the power plant operation [2,3].…”