A systematic literature review on machine learning applications at coal-fired thermal power plants for improved energy efficiency

Figures & data

Figure 1. Supervised learning algorithm (Liu 2011).

Figure 2. The support vector machine: Potential hyperplane (Gandhi 2018).

Figure 3. The deep neural networks (IBM Cloud Education 2020a).

Figure 4. The neural networks (Rosenblatt 1958).

Figure 5. Reinforcement learning algorithm (Bhatt 2019).

Table 1. The quality assessment process.

Phases	Description of each phase
Identification	The databases were searched using the defined keywords in each study's title, abstract and keywords. The following results were obtained: 1. Science Direct = 2 2. Scopus = 11 3. Web of Science = 0 4. IEEE Explore = 1 5. Emerald Insight = 0 Total = 14
Screening	The following results were obtained by screening the studies to ensure that each study adheres to the inclusion and exclusion criteria, as outlined in Section 3.3. 1. Science Direct = 2 2. Scopus = 11 3. Web of Science = 0 4. IEEE Explore = 1 5. Emerald Insight = 0 Total = 14
Eligibility	All duplicates were removed after screening each study to ensure the studies met the inclusion and exclusion criteria. No duplicates were identified. 1. Science Direct = 2 2. Scopus = 11 3. Web of Science = 0 4. IEEE Explore = 1 5. Emerald Insight = 0 Total = 14
Inclusion	A full-text assessment was done on all 14 studies to investigate machine learning applications at coal-fired thermal power plants for improved energy efficiency. The results are discussed in Section 4.

Table 2. Systematic literature review results.

#	Title of the study	Type of machine learning model applied	Year	Database	Reference
1	Digital twin in the energy industry: Proposed a robust digital twin for power plants and other complex capital-intensive large engineering systems.	A deep reinforcement learning (DLR) model	2022	Science Direct	Sleiti, Kapat, and Vesely (2022)
2	Energy digital twin technology for industrial energy management: Classification, challenges, and future.	Review paper: The existing literature on energy digital twin technology was investigated in this study. The authors highlighted the advantages, including improvements in energy management and optimisation, improved maintenance and servicing, and enhanced renewable energy. This study proposes an original multi-dimensional digital twin with guidelines demonstrating how these technologies can be applied to reduce the carbon footprint	2022	Science Direct	Yu et al. (2022)
3	Thermal calculations of a natural circulation power boiler operating under a wide range of loads.	Other – A methodology was developed specifically for the thermal flow of a natural circulation power boiler and contributed towards the power boiler's design process. Various in-house methods were formulated and used in conjunction with the literature. This method is developed to compute the heat transfer in the combustion gas duct's wall superheaters and hanger tubes. Following these calculations, a computer programme was developed. Various other CFD computations were performed that focused explicitly on the boiler furnace chamber. The results were compared to the data provided by the manufacturer. Only slight differences were observed, and the models were verified.	2022	Scopus	Zima et al. (2022)
4	A Review on the Adoption of AI, BC, and IoT in Sustainability Research	Review paper: Artificial intelligence (AI), blockchain (BC), and the internet of Things (IoT) are the factors influencing the advancement of sustainability the most. This study aimed to investigate how digital transformation drives natural and human systems by conducting a bibliometric analysis. The authors assessed 960 studies, concluding that most advancements were made in the following sectors: Smart cities, energy systems and supply chain management. Artificial intelligence contributed towards these advancements by assisting with scheduling, predicting, and monitoring energy systems within smart cities. In conclusion, these authors stated that there is an increase in the technological integration of AI and IoT, specifically in the smart city and energy sectors.	2022	Scopus	Wu et al. (2022)
5	The Implementation of K-Means Clustering in Coal-fired Power Plant Performance.	K-Means clustering	2022	Scopus	Wahyuningtyas and Asrol (2022)
6	A systematic review of big data in energy analytics using energy computing techniques	Review paper: Energy computing techniques in the energy-orientated domain were investigated in this study. The authors indicated an increase in energy analytics due to the rapid increase in data. In conclusion, this study highlighted that the thermal power plant, smart grid, and power systems datasets were extensively utilised, and the artificial neural network (ANN) was mainly applied.	2022	Scopus	Dhanalakshmi and Ayyanathan (2022)
7	Repowering Industrial Combined Heat and Power Units: a contribution to Cleaner Energy Production	Other – This study is classified as a gas turbine-based repowering study. Repowering industrial units results in a reduction of greenhouse gas emissions. This study specifically focused on fuel and carbon monoxide reduction. In conclusion, two conservative repowering options were identified, and both indicated feasible economics with a simple payback period.	2022	Scopus	Variny and Kšiňanová (2022)
8	Improvement of marine steam turbine conventional exergy analysis by neural network application	An Artificial neural network – A multi-layered perceptron (MLP)	2020	Scopus	Baressi Šegota et al. (2020)
9	Optimisation of a 660 MWE supercritical power plant performance: A case of industry 4.0 in the data-driven operational management. Part 2: power generation	The following machine learning methods were developed in this study: 1. Artificial neural network (ANN): 2. Multiple linear regression (MLR): 3. The least-square vector machine (LSSVM)	2020	Scopus	Muhammad Ashraf et al. (2020)
10	Artificial neural network and its applications in the energy sector – An overview	Review paper: This study aimed to summarise artificial intelligence methods’ significance in the energy sector. The authors indicated that artificial neural networks are effective methods designed to solve complex problems within modelling, control, and optimisation.	2020	Scopus	Babatunde (2020)
11	Data-driven framework for boiler performance monitoring	Other – This study aimed to propose a framework for monitoring a steam boiler's real-time operations. The boiler's actual efficiency was compared and monitored with the expected efficiency. The performance of the boiler is measured using a statistical process control chart. The framework was verified using fluidised bed boiler and corner-fired boiler historical data. In conclusion, the strongest correlations between the variables and boiler efficiency were consistent in both cases.	2016	Scopus	Nikula, Ruusunen, and Leiviskä (2016)
12	Neuro-genetic model for crude oil price prediction while considering the impact of uncertainties	An artificial neural network model combined with the genetic algorithm (neuro-genetic)	2016	Scopus	Chiroma and Abdulkareem (2016)
13	A methodology for energy savings verification in the industry with the application for a CHP (combined heat and power) plant	An Artificial neural network (ANN)	2015	Scopus	Rossi and Velázquez (2015)
14	Control Strategy for Denitrification Efficiency of a Coal-Fired Power Plant Based on Deep Reinforcement Learning	A deep reinforcement learning (DLR) model	2020	IEEE Explore	Fu et al. (2020)

Table 3. Describes how machine learning models were applied in each study.

#	Title of the study	An overview of each machine learning application	Contribution(s) and Future Research
1	Digital twin in energy industry: Proposed a robust digital twin for power plants and other complex capital-intensive large engineering systems	This study did not specifically apply to machine learning. However, deep learning is used to assist the authors in developing a database model. To achieve high reliability while considering cost, a digital twin (DT) architecture is required for future power plants. The paper consists of three sections: The authors provide an overview of digital twins and research regarding energy applications. The design requirements for the digital twin are established by considering physics-based formulation, sensor data analysis, statistical analysis, and the system Genome. Valuable future recommendations are provided. The authors highlighted that a data-driven approach alone is not sufficient for achieving improved reliability. They recommended that a low-order physics model should operate with the updated system parameters to enhance the results of the data-driven approach. The database model is designed to use ADL to detect and identify the differences between the dynamic systems model (DSM) and the digital twin (DT). The sensor failures are identified using deep learning and are sent back to the sensor database. The sensor database is updated accordingly. In conclusion, this study demonstrated the discrepancies between the dynamic system models (DSM), anomaly detection, and deep learning (ADL). Using operational data, the vector autoregressive model was applied for anomaly detection in utility gas turbines.	The contributions of this study include the following: A comprehensive overview of digital twins subject to the application, frameworks, and architectures for power plants. Research related to digital twins and how these can contribute towards energy savings applications. The authors also proposed a robust digital twin for power plants. Future research includes the following: Any digital twin requires actual data and a description representing a holistic view of the system, focusing explicitly on each part and connection. The authors suggested that data-driven algorithms capable of predicting dynamic system behaviour are still lacking in the literature. A data-driven model alone is not sufficient. For this reason, the authors proposed that a physics-based model must be integrated with system parameters to improve the interpretation of the results from the data-driven processes. The authors noted that research related to cyber security with digital twins had not been reported in the literature, which can be a future research study.
2	The Implementation of K-Means Clustering in Coal-fired Power Plant Performance	The k-Means Clustering algorithm is developed for coal-fired power plant performance. Two attributes are required for power plant performance: Equivalent Availability Factor (EAF) and Equivalent Outage Rate (EFOR) The data set used in this study is from 2020, and the data performance indices in these investigations are the abovementioned attributes. The authors obtained the data set from the Generation Availability Information System (GAIS) database. The authors pre-processed the data to ensure data integrity. A number of 576 numerical datasets of the two attributes were pre-processed. The relationship between the EAF and the EFOR was investigated to determine the characteristics of the power plant. The analysis concluded a negative correlation between the two attributes, and the authors did a further investigation. The features of the input data determined the number of clusters, and the Elbow method was applied to analyse the total number of clusters used explicitly for power plant clustering. After an in-depth investigation, 4 clusters were selected for optimal power plant performance. This number of clusters produced each cluster's highest EAF value and the lowest EFOR value In conclusion, the higher the EAF value and the lower the EFOR value, the better the plant performs. For this reason, the authors evaluated these values and made conclusions accordingly. Applying this machine learning technique, the results indicated which plant needs to be prioritised for maintenance.	The contributions of this study include the following: The authors demonstrated the ability of machine learning to analyse current power plant conditions successfully. Future research includes the following: A real dataset is required to control current conditions to improve the decision-making process. Furthermore, the authors illustrated that a decision-support system may facilitate the decision-maker in making faster and more reliability decisions.
3	Improvement of marine steam turbine conventional exergy analysis by neural network application.	Neural networks were applied to improve the steam turbine's conventional exergy. Subsequently, a multi-layered perceptron (MLP) model is developed in Python programming language using the Scikit-Learn library. The data set comprised 125 points for the ambient temperature, pressure, steam mass flow rate, and exergy destruction and efficiency values for the high-pressure cylinder, low-pressure cylinder, and the entire turbine. The input variables were the steam temperature, pressure, and mass flow rate. The weights of the inter-neuron connection and the values of the hidden layers are randomly defined. The output values are compared to the input values by focusing explicitly on exergy destruction or efficiency. Each data point has 23 input values and approximately six output values. The backward propagation algorithm is used to train the neural network, and this method is specifically used to adjust the weights based on the gradient of the error. The training process is repeated until a very low error is obtained for the training model. It is important to note that two models were trained for each possible output: exergy destruction and exergy efficiency. For this reason, this study had a total of 72 final models. One of the purposes of training the model is to identify the optimal hyperparameter values. The hyperparameters selected were the activation function of the hidden layer neurons, the total number of hidden layers, the total number of neurons per hidden layer, and the solver, which estimates the weights, the learning rate and the L2 regularisation parameter. In this study, Grid Search was used using the GridSearchCV Function in Python. The training of the model took place on a supercomputer. The coefficient of correlation ($R^2$) and the mean absolute error (MAE) were used to determine the model's performance. The K-fold cross-validation method was applied with ten folds, and the data set was randomly split into training and testing data.	The contributions of this study include the following: Developing a neural network to improve the marine steam turbine conventional exergy analysis. This study demonstrated the usefulness of Artificial intelligence and that these algorithms could be applied to calculate the exergy destruction and exergy efficiency of a marine steam turbine. Future research includes the following: The authors suggested that a future study can be conducted to determine the optimal turbine operating points, precisely steam temperature, pressure, and mass flow rate. These optimal operating points may be integrated with the MLP to analyse exergy parameter prediction focusing on the entire turbine. This study's same approach and technique can be applied to other marine steam turbines. To improve the prediction of the turbine exergy analysis parameter, the authors suggested that a study be conducted where extensive measurements over a long period are collected on the turbine and the turbine's cylinders. Using these measurements, a more accurate prediction can be made based on the operating period.
4	Optimisation of a 660 MWE supercritical power plant performance: A case of industry 4.0 in the data-driven operational management. Part 2: power generation.	This study considers 24 thermo-electric parameters of a specific power plant to model the generator power under different power generation scenarios. These operating parameters were obtained from the power plant's turbine, boiler, and generator sides. The data set consisted of 1900 data points. The following machine learning methods were developed in this study: Artificial neural network (ANN): The feedforward backpropagation algorithm is developed in this study. The authors used the gradient descent with momentum as a training function. The tangent hyperbolic and purelin were employed as transfer functions in both the hidden and output layer of the ANN model. Multiple linear regression (MLR): The least square vector machine (LSSVM) was used to develop this model. First, the difference (referred to as residuals) between the actual and observed values was calculated. Second, the sum of the square of residuals is minimised to obtain the best fit i.e. least squares minimisation was employed. The least-square vector machine (LSSVM): This model was trained based on the structure risk minimisation (SRM) principle. The relationship between the input and output variables is determined by using a Gaussian kernel function. All variables were standardised for model training. The authors used the Bayesian optimiser and the expected improvement per second in conjunction with the acquisition function to optimise the model under 30 epochs. After training the MLR, ANN and LSSVM models, the models were validated using new (unseen) data. The unseen data were defined as generator power data and were not included in the training dataset. The coefficient of determination, mean absolute percentage error (MAE), root-mean-square error (RMSE), normalised RMSE (NRMSE), and the mean absolute percentage error (MAPE) were estimated to analyse the model's robustness and effectiveness. The results demonstrated that the LSSVM outperformed the other two models in predicting the plant's generator power. This model was further used to predict the generator power of the plant while considering numerous operating strategies.	The contributions of this study include the following: The ability of a least-square vector machine (LSSVM) model is demonstrated in this study. This model outperformed the multiple linear regression (MLR) models and the artificial neural network (ANN). This model's reliable performance in modelling generator power has been highlighted in this study. In this study, the power plant's operation control has been improved by employing data-driven optimisation strategies.
5	Neuro-genetic model for crude oil price prediction while considering the impact of uncertainties	The authors proposed a neural network model combined with the genetic algorithm (neuro-genetic) to predict the crude oil price while considering uncertainty. The parameters optimised are the neural network's bias, weights, and hidden layer nodes. The values for the input neurons, the output neurons, the activation function, the weights, and the hidden layers were defined prior to training the model. The model was trained by splitting the dataset into 80% training and 20% testing. The optimal parameter values were obtained through trial and error, and the sensitivity of the neuro-genetic model was analysed. The models were trained on two datasets obtained from the Energy Information Administration of the US Department of Energy. The first dataset used in this study was obtained from May 1987 to July 1991, excluding uncertainties. The second data set, including uncertainties, was obtained from August 1990 to February 1991. The proposed method was implemented using Matlab 2012b. The performance of the neuro-genetic model was investigated and compared to the neural network and support vector machine. It was concluded that the neuro-genetic model performance is better due to accuracy and CPU processing time.	The contributions of this study include the following: The authors proposed an artificial intelligence approach to predict the crude oil price while considering uncertainties. The neuro-genetic model developed outperformed the neural network (NN) and the support vector machine (SVM). Considering uncertainty in decision-making provides a more realistic and practical application of crude oil prediction. Future research includes the following: Advancing and updating the current model to consider the prediction of crude oil in different prediction horizons. The framework may be modified to be applied to optimise nuclear data libraries and nitride fuel.
6	A methodology for energy savings verification in the industry with the application for a CHP (combined heat and power) plant	A methodology to assess the savings achieved from developing and implementing energy conservation measures (ECM) in specific industrial plants was developed. An artificial neural network was developed in this study. Historical data were obtained from the plant's distributed control system and are pre-processed to ensure data integrity. The data were cleaned by eliminating invalid and non-representative data points. The applicable variables were defined, and the non-applicable variables were removed from the dataset. The authors indicated that the ANN is famous for detecting non-linear patterns and controlling many industrial processes and power plants. The ANN is also capable of replicating these processes with satisfactory levels of accuracy. The data samples were determined for training, followed by the selection of input variables. The ANN was trained using the back error propagation algorithm. The model's performance was analysed using the coefficient of determination, the root mean square error (RMSE), and the mean absolute error (MAE). Acceptable model performance was confirmed.	The contributions of this study include the following: The authors proposed a methodology to evaluate the savings from implementing energy conservation measures in industrial plants. Future research includes the following: The authors suggested evaluating other techniques, excluding the ANN model, including the feature selection method. These methods could improve the screening stage of the dataset. It is also important to note that feature selection is essential when working with large datasets and ranking all variables’ relevance to the target variable.
7	Control Strategy for Denitrification Efficiency of a Coal-Fired Power Plant Based on Deep Reinforcement Learning	In this study, a 1000MW unit is used as a case study. A deep reinforcement learning (DLR) model is developed with a long short-term memory model, the Asynchronous Advantage Actor-Critic algorithm (A3C). The data used in this study was obtained from the plant's Distributed Control System (DCS). The authors used 10000 sets of data from a 1000MW coal-fired boiler. Variables, including, but not limited to, spray ammonia mass flow, Inlet mass concentration, boiler load, inlet mass and inlet flue gas temperature, were identified. The data was pre-processed, followed by using this data as input for the Long short-term memory neural network (LSTM). Subsequently, this LSTM model was used for the DRL's environment. The DLR method used the state value output by the environment as an input to produce a new updated action value. By conducting several iterations, a new control strategy of SCR was obtained. The Back Propagation algorithm was used to train the LSTM. For the training, six main variables were defined as input variables; the model consisted of 3 LSTM layers and 64 nodes per layer. The model was optimised using the Adam optimiser algorithm, and the data were split into 20% testing and 80% training data. Two performance metrics were used: the root mean square error (RMSE) and the R-squared. The accuracy of the efficiency calculated was 91.7%. In conclusion, it was shown that the approach is feasible and universally applicable.	The contributions of this study include the following: One of the primary contributions is that the approach proposed in this study demonstrates the ability to be feasible and universally applicable in industry. Future research includes the following: The authors indicated that the following should be further investigated: the control method of the combustion process, the system cost of coal-fired power plants and the denitrification process. These elements should be investigated to improve the stability and reliability of control strategies.

Figure 6. Search results: Publications per year and database.

Figure 7. The type of machine learning algorithms applied in each study.

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