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This research investigates the decarbonization of India's electricity grid using ammonia in power plants. It focuses on ammonia produced in Western Australia and transported to India, co-fired with high rank coal, and compared with power plants utilizing carbon capture and sequestration (CCS). The study assesses the overall costs and the life cycle greenhouse gas (LC GHG) emissions for both new plants and retrofits. For 20% gray, blue, and green ammonia, the levelized cost of electricity is 86, 89, 125 $/MWh, with corresponding LC GHG emissions of 1,234, 1,079, and 1,062 kg CO2e/MWh. Co-firing with green ammonia, though more expensive than blue ammonia, yields lower CO2 emissions. Conversely, reducing the same amount of direct CO2 emission via CCS costs $84/MWh and a LC GHG emission of 1,227 kg CO2e/MWh. While CCS is cheaper, it results in higher LC GHG. There is a trade-off between cost and emissions across the strategies. Under scenarios with low capacity factors or reduced ammonia production costs, coal-ammonia co-firing could become more economical and greener than the CCS. This study provides quantitative insights for policymakers and project developers. However, it is crucial for decision-makers to consider several factors: (1) the potential impact of social resistance to CCS; (2) the time required for large-scale commercialization of CCS technology, which is expected to be significantly longer than the implementation time for a coal-ammonia co-firing decarbonization strategy; (3) the potential of either CCS or ammonia-coal co-firing strategy to enhance India's electricity mix, thus contributing to energy security. [ABSTRACT FROM AUTHOR]

Power plants release a massive amount of waste heat at very low temperatures. Posing a challenge for efficient conversion into useful work through conventional methods. By selecting appropriate working fluids, low-grade power cycles can solve this problem by converting waste heat to electricity. The present work dealt with waste heat utilization using the Organic Rankine Cycle (ORC) from a 400 MWe coal based supercritical Pressurized Pulverized Combined Cycle (PPCC). The working fluid for the ORC is the refrigerant R245fa. High-ash (HA) Indian coal and low-ash (LA) South African coal are used as fuel to assess the plant's ability to produce electricity under ambient conditions in India. The simulation flowsheet program "Cycle-Tempo" models and simulates different plant layouts. The thermodynamic assessment unveiled that the standalone plant has energy and exergy efficiencies of 43.46% and 39.87% for HA & 45.42% and 43.84% for LA, respectively. According to this study, the proposed plant has energy and exergy efficiencies of 44.63% and 40.94% for HA & 46.69% and 45.07% for LA, respectively. Moreover, using ORC, the waste heat generates additional electricity of 9.38 MWe, with an energy efficiency of 12.92% and exergy efficiency of 34.63%. [ABSTRACT FROM AUTHOR]

Yenilenebilir enerji kaynaklarıyla elektrik üretimi üzerine araştırmalar hızlı bir şekilde ilerlese de fosil yakıtlarla elektrik üretiminin hem dünyada hem Türkiye’de uzun bir süre daha sürmesi beklenmektedir. Hem çevrede oluşturduğu zararlı etkiler hem de tükenme riskinin yüksek olması, fosil yakıtların termik santrallerde verimli bir şekilde tüketilmesini zorunluluk haline getirmiştir. Fosil yakıtın bir türü olan kömür, elektrik üretmek amacıyla termik santrallerde yaygın olarak kullanılmaktadır. Kömür yakıtlı termik santrallerde kullanılan kazan, süper kritik bir kazan seçildiğinde santral verimi artırılabilir. Bu çalışmada Zonguldak ilinde bulunan Eren Enerji’ye ait kömür yakıtlı süper kritik termik santralin termodinamik performansı, enerji ve ekserji analiz metotlarıyla incelenmiştir. Santralde bulunan her bir elemana ait enerji kayıpları ve tersinmezlikler hesaplanmıştır. Enerji analizi, kömürün kimyasal enerjisinin %48.5’inin kondenserde soğutma suyuna aktarıldığını; ekserji analizi ise kömürün kimyasal ekserjisinin %49.2’sinin kazanda yok edildiğini göstermiştir. Santralin enerji verimi %33.65, ekserji verimi ise %31.42 olarak hesaplanmıştır.

With increased interests in affordable energy resources, a cleaner environment, and sustainability, more objectives and operational obligations have been introduced to recent power plant control systems. This paper presents a verified load frequency model predictive control (MPC) that aims to satisfy the load demand of three practical generation technologies, which are wind energy systems, clean coal supercritical (SC) power plants, and dual-fuel gas turbines (GTs). Simplified state-space models for the two thermal units were constructed by concepts of subspace identification, whereas the individual wind turbine integration was implicated by the Hammerstein–Wiener (HW) model and then augmented from the output to simulate the effect of a wind farm, assuming similar power harvesting from all turbines in the farm. A practical strategy of control was then suggested, which was as follows: with a changing load demand, the available harvested wind energy must be fully admitted to the network to cover part of the load demand with the free energy, and the resultant load signal will then be instructed to the MPCs designed for the coal and gas units for the coordination of generation. The load signal, after being penetrated by wind, has more transients and faster changes, and needs a more sophisticated control in order to follow the load demand of the flexible coal and gas units. Furthermore, as the level of wind penetration increases, the power system frequency excursions are higher. The simulation results show an acceptable performance for linear MPCs embedded to the GT and coal units, with around a 90 MW share of wind without exceeding the safe restrictions of the plants and allowable reasonable frequency excursions. The complete simulation framework can be used to facilitate wind energy penetration in such power systems and train the operators and future engineers with subsequent power system frequency simulation studies. [ABSTRACT FROM AUTHOR]

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Many developing countries are opting to rely on coal-fired power mainly due to the abundance of the resource and its economic viability, since the capital needed to build a new coalfired power plant is not as large as a nuclear power plant. Compared to nuclear power, coal-fired power does not bear the risk of major catastrophe and therefore it will be relevant in the power industry for many years to come. However, the major drawback of coal-fired power plant is the fact that it is not a clean energy prospect and it is considered one of the main contributors to the world's greenhouse gases. Coal combustion is usually associated with the emission of greenhouse gases such as CO, CO2, NOx, and SO2. One of the main objectives of coal combustion research is to develop techniques that will help power plant operators to utilize coal cleanly and efficiently by adopting coal blending practices. Currently, emission mitigation and boiler cleanliness issues through coal blending process are focusing more on laboratory scale tests and not utilizing actual plant data and behaviour. This study evaluates the effectiveness of the implementation of Coal Combustion Prediction Analysis Tool (CPAT) as a tool to facilitate power plant operator in predicting the impact of individual or blended coal quality. It will provide early predictions on boiler combustion performance related to the coal quality and it will assist the power plant operators preparing for boiler process control optimization. This study will also discuss slagging and fouling factor of different coal type, which are used in one coal-fired power plant in South East Asia. [ABSTRACT FROM AUTHOR]

Energy consumption in India is increasing drastically due to the increase in its population every year. In this severe condition, it is highly desirable to focus on the performance of existing and upcoming power plants in India using thermal performance analysis of each plant. The subcritical power plants are going to be replaced by supercritical power plants (SUPP) in the present scenario in the country. The present study deals with the thermal performance analysis of a 660 MW capacity coal-fired SUPP situated in western India. The thermodynamic modeling with empirical relation of each of its components is done and executed in MATLAB programming interface. The pure sliding pressure with variation in plant load is taken for constructing a semi-empirical model. The properties of steam are considered as per the IAPWS IF-97 standard. The results obtained from the simulation model are validated with actual operational data of the plant. The amount of coal consumption, steam mass flow rate, plant overall efficiency with variation in plant load is analyzed and compared with the operational data. The output obtained from the simulated program found to be satisfactory by comparing it with the actual operational plant data under study. The condenser rejects 36.42% of total heat to cooling water. The predicted steam generator efficiency of 86.04% is validated with actual steam generator efficiency. The simulated model of the SUPP will help those technologists and investigators who are putting their effort into improving the performance of coal-fired power plants. [ABSTRACT FROM AUTHOR]

This work details the development of a physics-based equipment health monitoring framework for a supercritical boiler, using first-principles models to estimate the remaining useful life (RUL) of its components. The framework accounts for fatigue and creep life consumption, generating spatio-temporal variations in mechanical and thermal stress. Analysis of the stress profile throughout the boiler highlights the finishing superheater inlet steam header as a vulnerable location susceptible to damage from cycling operation. The framework also yields quantified uncertainty in the RUL projection for specific locations, accounting for uncertainties in material properties and boiler operation. Results indicate that operational uncertainties (e.g., seasonal variation and operational strategy) and material properties (e.g., rupture time coefficients, Young's modulus, yield strength, and coefficient of thermal expansion) significantly impact the RUL of the finishing superheater inlet steam header. Additionally, case studies demonstrate the use of the health monitoring framework as a predictive tool for operational planning under uncertainty, including scenarios with and without updates on the operation of the boiler. • Physics-based equipment health monitoring framework for a SCPC boiler developed. • Spatio-temporal variations in mechanical and thermal stress obtained. • Remaining useful life (RUL) estimated considering fatigue and creep life consumption. • Finishing superheater inlet steam header found to be a vulnerable location. • Impacts of operational uncertainties and material properties on RUL quantified. [ABSTRACT FROM AUTHOR]

Oxide scale control is one of the critical maintenance issues in fossil fuel power plant. Hence, the water treatment of the feed water has been changed from all-volatile treatment (AVT) to oxygenated treatment (OT) by added some amount of oxygen in the system to form a stable oxide and minimize the scale growth rate. In this work, the oxide scales formed on T12 water wall tube after one cycle of chemical cleaning treatment under OT condition in a supercritical coal power plant were characterized by using various methods. It was found that the oxide scale formed on the inner surface of the tube had a multi-layer structure with a porous outer layer consisting of Fe3O4 and Cr-rich α-Fe2O3 oxides and a dense inner layer consisting of Cr-rich spinel oxide. A Cr- and Mn-enriched outermost layer was also observed. Cavities, pits, and exfoliation were observed in the oxide scales. The cavities and grain boundaries behaved as the short path of the diffusion of oxygen into the metal. Intergranular oxidation was observed between the inner layer of oxide scale and matrix. The distribution, morphology, and chemical compositional of these oxides were assessed and discussed from a point of view of the suitability of current chemical cleaning solution and procedure which has been used for AVT descaling. [ABSTRACT FROM AUTHOR]

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.

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.

Chemical looping air separation (CLAS) is a novel and promising technology for oxygen production. This paper presents the application of CLAS to the supercritical power plant for MILD oxy‐combustion. Compared with the reference conventional supercritical power plant, the power generation efficiency of the CLAS integrated MILD oxy‐combustion plant is only reduced by about ~1.37% points at the baseline case. CO2 compression process imposes additional ~3.97% points efficiency penalty, which is inevitable to all of the CO2 capture technologies. The net power efficiency of the CLAS integrated MILD oxy‐combustion plant is ~37.37%. Even though a higher reduction reactor temperature could boost the power efficiency and a higher oxidization reactor temperature reversely decreases the power efficiency, the influence of reactor temperature is marginal. The performance of CLAS integrated MILD oxy‐combustion plant is not sensitive to excess CO2 and O2 ratio. Different oxygen carriers have different suitable operating region, but possess similar power efficiency. The carbon capture rate of the CLAS integrated MILD oxy‐combustion plant is up to ~100%, resulting in a virtually carbon‐free fossil power plant. Chemical looping air separation is integrated with MILD oxy‐combustion. Net power efficiency of the plant is 37.37% including CO2 compression. Sensitivity analysis is conducted. [ABSTRACT FROM AUTHOR]

3-E (Energy, Exergy, and Environment) analysis of Pressurized Pulverized Combined Cycle (PPCC) SubCritical (SubC) and SuperCritical (SupC) power plants of net capacity 400 MW e using High Ash (HA) Indian coal under Indian ambient conditions is performed to assess the potential of PPCC power plant for electricity generation in Indian energy sector. The study also includes the comparative performance of the plant using an imported Low Ash (LA) coal and a parametric study is performed to understand the effect of various parameters affecting the energy and exergy efficiencies of the PPCC plant. It is found that the plant energy efficiency of PPCC SubC and SupC plants using HA coal are 42.44% and 43.46%, respectively. The exergy efficiency of the same plant using HA coal are 38.94% and 39.87%, respectively. The energy balance shows that the maximum energy loss is observed in cooling water (24%) followed by loss in stack (22%). The exergy balance shows that the maximum exergy destruction in combustor (32%) followed by stack (7%). The environmental analysis reveals that the CO 2 , NO x , and SO x emissions are 426, 3.54, and 3.20 g/kWh, respectively. [ABSTRACT FROM AUTHOR]

A conventional exergy analysis can highlight the main components having high thermodynamic inefficiencies, but cannot consider the interactions among components or the true potential for the improvement of each component. By splitting the exergy destruction into endogenous/exogenous and avoidable/unavoidable parts, the advanced exergy analysis is capable of providing additional information to conventional exergy analysis for improving the design and operation of energy conversion systems. This paper presents the application of both a conventional and an advanced exergy analysis to a supercritical coal-fired power plant. The results show that the ratio of exogenous exergy destruction differs quite a lot from component to component. In general, almost 90% of the total exergy destruction within turbines comes from their endogenous parts, while that of feedwater preheaters contributes more or less 70% to their total exergy destruction. Moreover, the boiler subsystem is proven to have a large amount of exergy destruction caused by the irreversibilities within the remaining components of the overall system. It is also found that the boiler subsystem still has the largest avoidable exergy destruction; however, the enhancement efforts should focus not only on its inherent irreversibilities but also on the inefficiencies within the remaining components. A large part of the avoidable exergy destruction within feedwater preheaters is exogenous; while that of the remaining components is mostly endogenous indicating that the improvements mainly depend on advances in design and operation of the component itself.

An integrated advanced supercritical coal-fired oxyfuel power plant with a novel cryogenic CO2 separation and compression technology for high purity CO2 to suit injection for Enhanced Oil Recovery purposes is investigated. The full process is modelled in Aspen Plus® consisting of: an Air Separation Unit (ASU), an Advanced Supercritical Pulverised Fuel (ASC PF) power plant with a bituminous coal as feedstock, a steam cycle, and a Carbon dioxide Purification Unit (CPU). The proposed CPU process accommodates a distillation column with an integrated reboiler duty to achieve a very high purity CO2 product (99.9%) with constrained oxygen levels (100 ppm). This work presents a detailed analysis of the CO2 separation and compression process within the full power plant, including effective heat integration to reduce the electricity output penalty associated with oxyfuel CO2 capture. The results of this analysis are compared with previous studies and indicate that the combined application of process optimisation in the CPU and advanced heat integration with the power plant offer promising results: In this work a high purity CO2 product was achieved while maintaining 90% capture for a net plant efficiency of 38.02% (LHV), compared with a thermal efficiency of 37.76% (LHV) for a reference simulation of an ASC PF oxy-fired plant with advanced heat integration, providing a lower purity CO2 product. [ABSTRACT FROM AUTHOR]

The paper presents a literature review on the topic of vapour power plants working according to the two-phase thermodynamic cycle with supercritical parameters. The main attention was focused on a review of articles and papers on the vapour power plants working using organic circulation fluids powered with low- and medium-temperature heat sources. Power plants with water-steam cycle supplied with a high-temperature sources have also been shown, however, it has been done mainly to show fundamental differences in the efficiency of the power plant and applications of organic and water-steam cycles. Based on a review of available literature references a comparative analysis of the parameters generated by power plants was conducted, depending on the working fluid used, the type and parameters of the heat source, with particular attention to the needs of power plant internal load. [ABSTRACT FROM AUTHOR]

Abstract: The supercritical power plant analyzed in this paper consists of the following elements: a steam turbine, a hard-coal-fired oxy-type pulverized fuel boiler, an air separation unit with a four-end-type high-temperature membrane and a carbon dioxide capture unit. The electrical power of the steam turbine is 600 MW, the live steam thermodynamic parameters are 650°C/30 MPa, and the reheated steam parameters are 670°C/6 MPa. First of all the net efficiency was calculated as functions of the oxygen recovery rate. The net efficiency was lower than the reference efficiency by 9–10.5pp, and a series of actions were thus proposed to reduce the loss of net efficiency. A change in the boiler structure produced an increase in the boiler efficiency of 2.5–2.74pp. The range of the optimal air compressor pressure ratio (19–23) due to the net efficiency was also determined. The integration of all installations with the steam turbine produced an increase in the gross electric power by up to 50.5 MW. This operation enabled the replacement of the steam regenerative heat exchangers with gas–water heat exchangers. As a result of these alterations, the net efficiency of the analyzed power plant was improved to 5.5pp less than the reference efficiency. [Copyright &y& Elsevier]

The objective of the paper is to analyse thermodynamical and operational parameters of the supercritical power plant with reference conditions as well as following the introduction of the hybrid system incorporating ORC. In ORC the upper heat source is a stream of hot water from the system of heat recovery having temperature of 90 oC, which is additionally aided by heat from the bleeds of the steam turbine. Thermodynamical analysis of the supercritical plant with and without incorporation of ORC was accomplished using computational flow mechanics numerical codes. Investigated were six working fluids such as propane, isobutane, pentane, ethanol, R236ea and R245fa. In the course of calculations determined were primarily the increase of the unit power and efficiency for the reference case and that with the ORC. [ABSTRACT FROM AUTHOR]

The paper presents a thermodynamic optimization of supercritical coal fired power plant. The aim of the study was to optimize part of the thermal cycle consisted of high-pressure turbine and two chosen highpressure feed water heaters. Calculations were carried out using IPSEpro software combined with MATLAB, where thermal efficiency and gross power generation efficiency were chosen as objective functions. It was shown that the optimization with newly developed framework is sufficiently precise and its main advantage is the reduction of computation time on comparison to the classical method. The calculations have shown the tendency of the increase in efficiency, with the rise of a number of function variables. [ABSTRACT FROM AUTHOR]

In the paper presented is an idea of organic Rankine cycle (ORC) operating with supercritical parameters and so called dry fluids. Discussed is one of the methods of improving the effectiveness of operation of supercritical cycle by application of internal regeneration of heat through the use of additional heat exchanger. The main objective of internal regenerator is to recover heat from the vapour leaving the turbine and its transfer to the liquid phase of working fluid after the circulation pump. In effect of application of the regenerative heat exchanger it is possible to obtain improved effectiveness of operation of the power plant, however, only in the case when the ORC plant is supplied from the so called sealed heat source. In the present paper presented is the discussion of heat sources and on the base of the case study of two heat sources, namely the rate of heat of thermal oil from the boiler and the rate of heat of hot air from the cooler of the clinkier from the cement production line having the same initial temperature of 260 °C, presented is the influence of the heat source on the justification of application of internal regeneration. In the paper presented are the calculations for the supercritical ORC power plant with R365mfc as a working fluid, accomplished has been exergy changes and exergy efficiency analysis with the view to select the most appropriate parameters of operation of the power plant for given parameters of the heat source. [ABSTRACT FROM AUTHOR]

A conventional exergy analysis can highlight the main components having high thermodynamic inefficiencies, but cannot consider the interactions among components or the true potential for the improvement of each component. By splitting the exergy destruction into endogenous/exogenous and avoidable/unavoidable parts, the advanced exergy analysis is capable of providing additional information to conventional exergy analysis for improving the design and operation of energy conversion systems. This paper presents the application of both a conventional and an advanced exergy analysis to a supercritical coal-fired power plant. The results show that the ratio of exogenous exergy destruction differs quite a lot from component to component. In general, almost 90% of the total exergy destruction within turbines comes from their endogenous parts, while that of feedwater preheaters contributes more or less 70% to their total exergy destruction. Moreover, the boiler subsystem is proven to have a large amount of exergy destruction caused by the irreversibilities within the remaining components of the overall system. It is also found that the boiler subsystem still has the largest avoidable exergy destruction; however, the enhancement efforts should focus not only on its inherent irreversibilities but also on the inefficiencies within the remaining components. A large part of the avoidable exergy destruction within feedwater preheaters is exogenous; while that of the remaining components is mostly endogenous indicating that the improvements mainly depend on advances in design and operation of the component itself. [ABSTRACT FROM AUTHOR]

Abstract: The efficiency of coal-fired power plant depends on various operating parameters such as main steam/reheat steam pressures and temperatures, turbine extraction pressures, and excess air ratio for a given fuel. However, simultaneous optimization of all these operating parameters to achieve the maximum plant efficiency is a challenging task. This study deals with the coupled ANN and GA based (neuro-genetic) optimization of a high ash coal-fired supercritical power plant in Indian climatic condition to determine the maximum possible plant efficiency. The power plant simulation data obtained from a flow-sheet program, “Cycle-Tempo” is used to train the artificial neural network (ANN) to predict the energy input through fuel (coal). The optimum set of various operating parameters that result in the minimum energy input to the power plant is then determined by coupling the trained ANN model as a fitness function with the genetic algorithm (GA). A unit size of 800MWe currently under development in India is considered to carry out the thermodynamic analysis based on energy and exergy. Apart from optimizing the design parameters, the developed model can also be used for on-line optimization when quick response is required. Furthermore, the effect of various coals on the thermodynamic performance of the optimized power plant is also determined. [Copyright &y& Elsevier]

This article describes the validation of a supercritical steam cycle. The cycle model was created with the commercial program GateCycle and validated using in-house code of the Institute of Power Engineering and Turbomachinery. The Institute's in-house code has been used extensively for industrial power plants calculations with good results. In the first step of the validation process, assumptions were made about the live steam temperature and pressure, net power, characteristic quantities for high- and low-pressure regenerative heat exchangers and pressure losses in heat exchangers. These assumptions were then used to develop a steam cycle model in GateCycle and a model based on the code developed in-house at the Institute of Power Engineering and Turbomachinery. Properties, such as thermodynamic parameters at characteristic points of the steam cycle, net power values and efficiencies, heat provided to the steam cycle and heat taken from the steam cycle, were compared. The last step of the analysis was calculation of relative errors of compared values. The method used for relative error calculations is presented in the paper. The assigned relative errors are very slight, generally not exceeding 0.1%. Based on our analysis, it can be concluded that using the GateCycle software for calculations of supercritical power plants is possible. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the influence of membrane separation of CO2 from flue gases and the impacts of the whole CCS process (CO2 separation and compression) on the performance of a coal-fired power plant are studied. First, the effects of the characteristics of the membrane (selectivity and permeability) and the parameters of the process (feed and permeate pressure) on two indices, CO2 recovery rate and CO2 purity are analysed. Next, a method for determining the minimum power loss and efficiency loss of the power plant as a function of these calculated indices is described. Then, the power requirements and efficiency loss (up to 15.4 percentage points) because of the CCS installation are calculated. A method for reducing these losses through the integration of the CCS installation with the power plant is also proposed. The main aims of the integration are heat exchange between media and a decrease in the CO2 temperature before compression. Implementing this process can result in a significant reduction of the efficiency loss by 8 percentage points. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]