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 MW_e 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.

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 MW_e 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.

Solar radiation from reanalysis datasets can be used to estimate solar energy potential in the absence of long-term surface measured data. Comprehensive validation of reanalysis data against short-term measurements of solar radiation is necessary to find uncertainties. The evaluation of the performance of seven reanalysis datasets (ERA-5, MERRA-2, ERA-Interim, JRA-55, NCEP-NCAR, NCEP-DOE, and CFSR) for Balochistan against high-quality surface measured data was performed using statistical analysis. The relative mean bias error (rMBE), relative mean absolute error (rMAE), and relative root mean square error of reanalysis solar radiation range from −4 to 7%, 8 to 14%, and 16 to 21%, respectively, using sub-daily data of six datasets except for NCEP-NCAR. Seasonal statistical analysis was performed to study seasonal variation of performance of datasets, which reveals the significant variation of errors in different seasons. Analysis of monthly mean data shows that rMBE and rMAE are less than 3 and 5% for ERA-5, respectively. The performance of the dataset changes by changing the temporal resolution of the data used for comparison. The monthly mean values of data show better performance, but the accuracy of data is compromised. The solar radiation data of ERA-5 can be used for preliminary solar resource assessment and power estimation.

Friction and wear are very crucial aspects of the performance, service life, and the operational costs for a mechanical component or equipment. To reduce the friction and wear at the interface of the sliding or mating parts, different conventional binary coatings like TiN, CrN, TiC, etc., have been used in the last two decades. But ternary nitride coatings have replaced the binary coatings due to better tribo-mechanical properties. Now, ternary nitride coatings are being extensively used in several fields such as cutting tools, machinery parts, orthopedic implants, microelectronics, marine equipment, decorative purposes, automotive, aerospace industry, etc. Many researchers have developed and investigated the ternary nitride coatings for different applications. Nonetheless, there is a huge research potential in the development and optimization of the tribo-mechanical properties of the ternary nitride coatings. Therefore, tribo-mechanical studies of the ternary nitride coatings are needed for fostering the new industrial applications. This paper is focused to summarize and compare the tribo-mechanical properties of the ternary nitride coatings comprehensively and aims to explore the novel research directions in the development of the ternary nitride coatings.