# Reducing Emissions Using Artificial Intelligence in the Energy Sector: A Scoping Review
## 3 Results
### AI Applications in Energy Generation for Emission Reduction
The authors reported values between 77.5% and 87.0% in thermal efficiency and values between 577.9 t/y and 597.6 t/y in CO\({}_{2}\) emissions in the Pareto-optimal front. The authors suggested that the results contribute to improvements in low-carbon production of hydrogen using the SMR method. [PERSON] et al. [48] studied the optimization and emission reduction in a 660 MW super-critical coal fired power plant. The authors modeled the power plant heat rate with seven plant operating parameters as the input features. The tested ML techniques were artificial neural network (ANN), support vector machine (SVM), and AutoML platform where Light Gradient Boosting on the ElasticNet Predictions model was used. The training dataset consisted of approximately 31,200 samples of historical data from the power plant supervisory information system. The model performances were validated with an external validation dataset. The ANN achieved the best results. The authors conducted a parametric study for the plant operating parameters using the Monte Carlo simulation technique with the ANN and SVM models and compared the results to the AutoML platform feature impact analysis. Based on the parametric study, the authors optimized the operating parameters of the power plant and estimated that the optimized operation parameterswould reduce CO\({}_{2}\) emissions by 173.4 kt/y, 220.7 kt/y, and 204.9 kt/y in 50%, 75%, and 100% power generation modes, respectively. In a separate study, [PERSON] and [PERSON] [51] used artificial neural networks to model presumably the same 660 MW coal plant operation and used nonlinear programming methods to find the optimal parameters where the model generated a maximum amount of power under the defined constraints. The model explanatory variables were Coal Flow Rate, Main Steam Temperature, Main Steam Flow Rate, and Reheat Steam Temperature. The target variable for the model was the power production of the coal plant. The optimized parameters were tested in a real setting by running the power plant while trying to maintain the parameters around the optimized values. The optimization achieved 3 t/h savings in Coal Flow Rate, 1.3% improvement in thermal efficiency, and estimated 50.5 kt/y accumulated emission reductions when compared to a business-as-usual setting where the plant is run with non-optimized parameters. In a third study, [PERSON] et al. [52] proposed two advanced AI modeling algorithms to improve the isentropic efficiency of a high-pressure (HP) steam turbine. The authors tested artificial neural network (ANN) and support vector machine (SVM) algorithms to model the process. Both models were trained and validated with a dataset that was collected from the power plant's supervisory information system, which includes data from the fuel combustion system, turbines, and reheating systems. The dataset contains hundreds of parameters of operational data of which the model input parameters were selected based on literature review and recommendations by the power plant operation and performance engineers. The modeled output parameter was the HP turbine efficiency. The authors used Monte Carlo methods to conduct a sensitivity analysis on the input variables and used nonlinear programming methods to find the optimal operating parameters under specific constraints. The results indicate that the efficiency of the HP turbine can be enhanced by 1.43%, 5.09%, and 3.40% for half-load, mid-load, and full-load power generation modes, respectively, when compared to the average input parameter values. Additionally, the annual CO\({}_{2}\) reduction is projected to be 58.3, 123.5, and 70.8 kilotons per year (kt/y) for half-load, mid-load, and full-load modes, respectively. Furthermore, SO\({}_{2}\), CH\({}_{4}\), N\({}_{2}\)O, and Hg emissions saw significant reductions across all three power generation modes of the power plant. [PERSON] et al. [53] studied how machine learning methods can be used to model and optimize a system with combined solar/natural gas power and freshwater cogeneration system.