Combining design of experiments, machine learning, and principal component analysis for predicting energy consumption and product quality of a natural gas processing plant

Abstract

Processing natural gas, as a widely used source of energy in our life, is imperative to eliminate the impurities in order to make it consumable. So, appropriate modeling of different units in a real gas processing plant (GPP) is an essential research field. Moreover, high-dimensional data, with probably unnecessary information, gathered from a real application may lead to complicated models. As a result, the original dataset, obtained through a three-level

The Role of Machine Learning and Design of Experiments in the Advancement of Biomaterial and Tissue Engineering Research

“…Therefore, the combination of DoE methods with ML holds the potential to further enhance the optimisation within the field of TE. This approach has been successfully applied in product innovation [ 108 ], and the chemical [ 109 ] and energy consumption industries [ 110 ]. DoE data has been used previously in ML algorithms to optimise the initial parameter settings ( Figure 10 ) [ 106 ].…”

“…In addition, the use of ML has also helped the aim of DoE by detecting the optimal factors and interactions ( Figure 10 ), where the final ML algorithm proposes the next experimental configuration. Therefore, this strategy is often referred to as “active learning” [ 111 ] since it puts the learner in control of the data and from that, the machine learns [ 105 , 109 , 112 ].…”