Abstract Reducing CO2 emissions from fossil fuel fired power plants has been a major environmental concern over the last decade. Amongst various carbon capture and storage (CCS) technologies, the utilization of solvent-based post-combustion capture (PCC), played a major role in the reduction of CO2 emissions. This paper illustrates the development of machine learning models to predict the outputs of the (PCC) unit. A fine tree, Matern Gaussian process regression (GPR), rational quadratic GPR and squared exponential GPR models were developed and compared with a feed-forward artificial neural network (ANN) model. An accuracy of up to 98% in predicting the o process outputs was achieved. Furthermore, the models were utilized to determine the optimum operating conditions for the process using sequential quadratic programming algorithm (SQP) and genetic algorithm (GA). The ue of the machine learning models has proven to be very useful since the complete mechanistic model is too large and tits run time is too long to allow for rigorous optimal solutions. The machine learning models and optimization problem were developed and solved using MATLAB. The data used in this work was obtained from simulating the process using gPROMS process builder. The inputs of the model were selected to be reboiler duty, condenser duty, reboiler pressure, flow rate, temperature, and the pressure of the flue gas. The models were able to accurately predict the outputs of the process which are the system energy requirements (SER), capture rate (CR) and the purity of condenser outlet stream (PU).

中文翻译：

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一种用于 CO2 燃烧后捕集装置建模和优化的机器学习方法

摘要 在过去十年中，减少化石燃料发电厂的 CO2 排放一直是一个主要的环境问题。在各种碳捕获和封存 (CCS) 技术中，基于溶剂的燃烧后捕获 (PCC) 的利用在减少二氧化碳排放方面发挥了重要作用。本文阐述了机器学习模型的发展，以预测 (PCC) 单元的输出。开发了一种精细树、Matern 高斯过程回归 (GPR)、有理二次 GPR 和平方指数 GPR 模型，并与前馈人工神经网络 (ANN) 模型进行了比较。在预测过程输出方面达到了高达 98% 的准确度。此外，这些模型用于确定使用顺序二次规划算法 (SQP) 和遗传算法 (GA) 的过程的最佳操作条件。机器学习模型的 ue 已被证明是非常有用的，因为完整的机械模型太大，tits 运行时间太长，无法提供严格的最优解。机器学习模型和优化问题是使用 MATLAB 开发和解决的。这项工作中使用的数据是通过使用 gPROMS 流程构建器模拟流程获得的。模型的输入被选择为再沸器负荷、冷凝器负荷、再沸器压力、流速、温度和烟道气压力。这些模型能够准确预测过程的输出，即系统能源需求 (SER)，