Design optimization and control of dividing wall column for purification of trichlorosilane

三氯氢硅 再沸器 控制理论(社会学) 模型预测控制 模拟退火 超调(微波通信) 占空比 控制(管理) 体积流量 PID控制器 计算机科学 工程类 温度控制 数学 数学优化 材料科学 电压 控制工程 机械工程 机械 热交换器 人工智能 物理 电气工程 冶金
作者
Haohao Zhang,Ping Lü,Zhe Ding,Yingbo Li,Hai Li,Chao Hua,Zhe Wu
出处
期刊:Chemical Engineering Science [Elsevier]
卷期号:257: 117716-117716 被引量:28
标识
DOI:10.1016/j.ces.2022.117716
摘要

• TA C appr . is proposed as a novel objective function for DWC parameter optimization. • Optimal energy control structure can be achieved by manipulating liquid split ratio. • MPC-PI hybrid structure balances both control performance and safety performance. • Simulated annealing algorithm is employed to optimize the weights of MPC controller. Polysilicon quality and energy consumption are directly affected by the purification process of trichlorosilane. In this work, the dividing wall column (DWC) as a promising energy-saving technology is utilized for trichlorosilane purification, with the design optimization carried out using steady-state simulation. Compared with conventional distillation process, DWC can reduce total annual cost (TAC), CO 2 emissions and exergy loss ( El ) by 35.81%, 53.56% and 54.10% on average, respectively. Established on the characteristics of superfractionator, four control structures are proposed in this work, two of which are multi-loop proportional-integral (PI) control schemes including condenser duty ( C ), distillate flow rate ( D ), bottom flow rate ( B )/reflux flow rate ( R ), side flow rate ( S ), reboiler duty ( V ) control structure and optimal energy control structure. The other two are model predictive control (MPC) schemes including standard MPC structure and MPC-PI hybrid structure. The weights of MPC controller are optimized using the simulated annealing (SA) algorithm. Dynamic simulation results demonstrate that MPC schemes achieve improved closed-loop performance in terms of minor overshoot, shorter transition time and reduced oscillation than PI control schemes. The integral absolute error (IAE) is introduced to further quantitatively evaluate MPC schemes. The simulation results demonstrate that the standard MPC structure achieves the best control performance and the MPC-PI hybrid structure enhances process safety while maintaining the desired control performance.
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