The distillation process is one of the most common and energy-intensive processes in the chemical industry. Most chemical processes are nonlinear and complex, because of which, it is difficult to find optimal operating conditions. To solve this problem, we developed a framework for energy optimization of the distillation process based on a machine learning (ML) model. The framework enables the efficient operation of the process by using the optimal operating conditions recommended by the ML-based predictive model. The predictive model, which is a key component, is developed in three steps: learning, validation, and improvement. In the learning step, we select an algorithm suitable for the purpose of the process and learn process data. In the validation step, the model is validated using hold-out cross-validation. Finally, in the improvement step, the model performance is improved through hyper-parameter optimization. We applied the framework to a commercial mixed butane distillation columns of 45,000 metric tons per annum capacity. The predictive model was based on commercial process data, and it can be used to predict the temperature at the product stage. The model recommended the steam flow rate required to maintain the target temperature of the product stage as per the operating conditions. The recommended steam flow rate will be guideline for the on-site operator. The software is developed that the predictive model can be easily applied to the commercial processes, and it identifies the state of the process and recommends optimal operating conditions.
|Number of pages||12|
|Journal||Energy Science and Engineering|
|Publication status||Published - 2022 Jun|
Bibliographical noteFunding Information:
This study has been conducted with the support of the Korea Institute of Industrial Technology as “Development of AI Platform Technology for Smart Chemical Process (kitech JH-21-0005)” and “Development of Global Optimization System for Energy Process (kitech EM-21-0022, IR-21-0029, and IZ-21-0052).”
© 2022 The Authors. Energy Science & Engineering published by Society of Chemical Industry and John Wiley & Sons Ltd.
All Science Journal Classification (ASJC) codes
- Safety, Risk, Reliability and Quality