Data analysis-based framework for the design and assessment of chemical process plants: a case study in amine gas-treating systems

Abstract

This work presents a process-integrity assessment framework to chemical process design that combines first principles, heuristics, vendor specifications, standards/codes, data analysis, and machine learning modelling, hypothesized as an efficient route for optimal process design. Our case study, a gas treating unit, illustrates its implementation compared with traditional process guidelines. Surrogate models are fitted with hybrid data from process simulation and plant values, supporting the integration between process and integrity values, as well as equipment sizing and cost estimation. Considerable errors are obtained when estimating design duty (1.4%–8.7%) and power requirements (11.1%–33.5%) of the main equipment. Potential sources of these deviations might be attributable to the inherent simplification of process guidelines and intrinsic noise of the plant data used for fitting surrogate models. The process design is then assessed by evaluating process variables and corrosion rate within an operational envelope, showing the synergy and integration of these variables. The benefits and challenges of this approach are drawn while future work in engineering education is presented for its future implementation and effectiveness assessment in enhancing the process design workflow.