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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.

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BACKGROUND:The accumulative municipal solid waste (MSW) production calls for emerging efficient technology for its handling. Anaerobic digestion (AD) of MSW provides effective waste volatilization. The high C/N ratio of MSW and increasing organic loading rates (OLRs) are central AD restrictions that affect the AD process performance, inhibition due to aggregation of volatile fatty acids (VFAs), and rapid fall in pH.RESULTS: This study examines the consequence of OLRs on AD of MSW with a high C/N ratio of 406 in food waste (FW) to have a balanced C/N ratio of 30. Three batch scale digesters investigate under mesophilic conditions (35 °C) with OLRs of 10, 15, and 20 gVS L −1 to assess the digester performance and stability in biogas yield, methane yield, and volatile solids (VS) reduction. The cumulative biogas and methane yield are observed to be 1336 and 776 mL gVS −1 , respectively, with a VS reduction rate of 78%, at 10 gVS L −1 . VFA/alkalinity ratio ranges from 0.02 to 0.01 at OLR of 10 and 15 gVS L −1 , which designates a higher buffering capability of the digester. While VFA/alkalinity ratio of 0.48 observes at OLR of 20 gVS L −1 . A rapid deprivation in digester performance and stability finds at ORL of 15 and 20 gVS L −1 . The cumulative biogas yield and methane yield decrease with the increase in OLR from 10 to 20 gVS L −1 .CONCLUSION: This study provided sufficient information for better AD processes and operational circumstances that are an optimum and effective method to convert organic matter to biogas fuel.

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Accurately predicting fuel blends' lower heating values (LHV) is crucial for optimizing a power plant. In this paper, we employ multiple artificial intelligence (AI) and machine learning‐based models for predicting the LHV of various fuel blends. Coal of two different ranks and two types of biomass is used in this study. One was the South African imported bituminous coal, and the other was lignite thar coal extracted from the Thar Coal Block‐2 mine by Sind Engro Coal Mining Company, Pakistan. Two types of biomass, that is, sugarcane bagasse and rice husk, were obtained locally from a sugar mill and rice mill located in the vicinity of Sahiwal, Punjab. Bituminous coal mixture with other coal types and both types of biomass are used with 10%, 20%, 30%, 40%, and 50% weight fractions, respectively. The calculation and model development procedure resulted in 91 different AI‐based models. The best is the Ridge Regressor, a high‐level end‐to‐end approach for fitting the model. The model can predict the LHV of the bituminous coal with lignite and biomass under a vast share of fuel blends.

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