Knowledge-based operation optimization of a distillation unit integrating feedstock property considerations

Abstract

The distillation unit (DU) is an essential product separation unit in refineries. The process operation of DU is directly related to the quality and yield of the final petroleum products. The DU studied in this work is deeply troubled by the varying feedstock properties, which aggravates the difficulty of process operation. To determine the proper operation variables, a knowledge-based operation optimization (KOO) strategy of a DU is proposed in this paper. The KOO strategy is composed of a supervision module and an optimization module. First, the operating conditions are divided into four types based on the feedstock properties. In supervision module, an improved bar-shaped convolutional neural network supervision model (IBS-CNN-based SM) is developed to monitor the operating conditions. The model output which represents the current operating condition information is transmitted to the lower optimization module. In optimization module, the fuzzy-logic-based optimization strategy is designed to adjust two temperature variables — the top temperature of the distillation column (TTDC) and the outlet temperature of the re-boiling furnace (OTRF) to ensure the product quality requirements. Industrial experiments have illustrated the KOO strategy could adapt to the varying feedstock properties. During the experiment, the proposed KOO strategy improved the product qualification rate from 86.67% to 93.34% and saved the consumption of gas and cooling water to a certain extent.