Optimization of a saturated gas plant: Meticulous simulation-based optimization - A case study

Applying deep learning-based regional feature recognition from macro-scale image to assist energy saving and emission reduction in industrial energy systems

INTRODUCTION

Image recognition technology has immense potential to be applied in industrial energy systems for energy conservation. However, the low recognition accuracy and generalization ability under actual operation conditions limit its commercial application.

OBJECTIVES

To improve the recognition accuracy and generalization ability, a novel image recognition method integrating deep learning and domain knowledge was applied to assist energy saving and emission reduction for industrial energy systems.

METHODS

As a typical industrial scenario, the defrosting control in the refrigeration system was selected as the specific optimization object. By combining deep learning algorithm with domain knowledge, a residual-based convolutional neural network model (RCNN) was proposed specifically for frosty state recognition, which features the residual input and average pooling output. Based on the real-time recognition of frosty levels, a defrosting control optimization method was proposed to initiate and terminate the defrosting operation on demand.

RESULTS

By combining the advanced image recognition technique with specific energy domain knowledge, the proposed RCNN enables both high recognition accuracy and strong generalization ability. The recognition accuracy of RCNN reached 95.06% for the trained objects and 93.67% for non-trained objects while that of only 75.86% for the conventional CNN. By adopting the presented system optimization method assisted by RCNN, the defrosting frequency, accumulated time and energy consumption were 53.8%, 57.02% and 34.5% less than the original control method. Furthermore, the environmental and cost analysis illustrated that the annual reduction in CO₂ emissions is 2145.21 to 3412.84 kg and the payback time was less than 2.5 years which was far below the service life.

CONCLUSION

The technical feasibility and significant energy-saving benefits of deep learning-based image recognition method were demonstrated through the field experiment. Our study shows the great application potential of image recognition technology and promotes carbon neutrality in industrial energy systems.

Simulation-Based Optimization of a Multiple Gas Feed Sweetening Process

For an energy-intensive sweetening process, it is common that sour gases from different sources are sent to a single sweetening plant in industries. In our previous work, a multiple gas feed sweetening process was proposed, which can simultaneously improve the purity of H₂S and reduce the energy consumption of the plant. This study aims to develop the superstructure of that process and use a simulation-based optimization framework with Aspen HYSYS as the process simulator and particle swarm optimization algorithm as the optimizer. In addition, by taking full advantage of the robustness of the built-in algorithm of the simulator, the convergence of the model is improved; meanwhile, simplification of the process and reduction of the optimization time are accessible with the proposed design specifications and assumptions. For a convergence-difficult column, a stepwise convergence adjustment was used to ensure their convergence. Based on this, the robustness and effectiveness of the method is proven through a case study, and it can also provide guidance for model selection, process simplification, and optimization of the same type of absorption process.