# Reducing Emissions Using Artificial Intelligence in the Energy Sector: A Scoping Review
## 3 Results
### AI Applications in Energy Generation for Emission Reduction  
The largest category with the most papers contains studies related to energy generation optimization with AI methods. A common theme between most of the papers is to fit a machine learning model to a dataset that is collected from the energy generation process and use an optimization algorithm to find the optimal input parameters for the model. Different variants of artificial neural networks were the most common method for modeling the processes, although many of the studies tested multiple methods for process modeling. Different optimization algorithms were used in the studies, such as Particle Swarm Optimization, nonlinear programming methods, Monte Carlo simulation, and Ant Colony Optimization. Figure 2: PRISMA flowchart illustrating the number of papers through different phases of the review process. The related review studies that were identified in the contents screening phase are listed in the related studies section of this paper. [PERSON] et al. [49] studied how to optimize the scheduling of an adjustable post-combustion carbon capture (PCC) system in a coal fired power plant (CFPP) with integrated renewable power generation. The PCC system works by absorbing the CO\({}_{2}\) to a solvent. The CO\({}_{2}\) is released from the solvent when it is heated, therefore regenerating the solvent for reuse. Some of the energy required for solvent heating can be generated with renewables, such as wind or solar. The operational limits of the CFPP, renewable power availability, PCC system adjustment, CO\({}_{2}\) emissions, and load demand create a complex optimization problem with different solutions for different objective functions. In this paper, the authors optimized the PCC system timings so that the regional power grid operating costs were minimized. One part of the optimization target was the penalty costs of CO\({}_{2}\) emissions. The authors used a PCC system modeling platform to create a dataset with 3000 samples. A total of 2000 samples were used to train a deep belief network (DBN) and the rest were used for model validation. The authors used the Particle Swarm Optimization method on the DBN model to optimize the objective function. The authors reported that when the PCC system is optimally scheduled, the whole CFPP-PCC system renewable power curtailments can be reduced by 51% with CO\({}_{2}\) emission reductions by 80% when compared to standalone CFPP. [PERSON] et al. [50] optimized the steam methane reforming (SMR) process for hydrogen production to produce less CO\({}_{2}\) emissions. Even though hydrogen fuel cells do not produce emissions when the gas is converted to electricity, there are multiple ways to produce hydrogen gas and some of them do produce CO\({}_{2}\) emissions. SMR is one such method where fossil fuels are used to produce hydrogen. In this paper, authors modeled an SMR production plant using deep neural networks (DNNs). The dataset was collected from an SMR pilot plant which included 66 data collection points. However, the pilot plant did not include measurements for composition and flow rate of CO\({}_{2}\); therefore, those measurements needed to be simulated with a process simulation model. Two DNN models were trained with the data, which the authors named the operation DNN model and the simulation DNN model. The operation model was trained with the data that were collected from the pilot plant. However, because the collected data did not have values for the composition and flow rate, the simulation model training data included simulated values for those variables. The operation model was used in simulation model validation since many of the output variables were shared between the two models. The authors used a multi-objective particle swarm optimization method to optimize the input parameters for the simulation DNN model. The optimization targets were to maximize the thermal efficiency and minimize CO\({}_{2}\) emissions.