Abstract: Dividing wall batch distillation with middle vessel (DWBDM) is a new type of batch distillation column, with outstanding advantages of low capital cost, energy saving and flexible operation. However, temperature control of DWBDM process is challenging, since inherently dynamic and highly nonlinear, which make it difficult to give the controller reasonable set value or optimal temperature profile for temperature control scheme. To overcome this obstacle, this study proposes a new strategy to develop temperature control scheme for DWBDM combining neural network soft-sensor with fuzzy control. Dynamic model of DWBDM was firstly developed and numerically solved by Python, with three control schemes: composition control by PID and fuzzy control respectively, and temperature control by fuzzy control with neural network soft-sensor. For dynamic process, the neural networks with memory functions, such as RNN, LSTM and GRU, are used to handle with time-series data. The results from a case example show that the new control scheme can perform a good temperature control of DWBDM with the same or even better product purities as traditional PID or fuzzy control, and fuzzy control could reduce the effect of prediction error from neural network, indicating that it is a highly feasible and effective control approach for DWBDM, and could even be extended to other dynamic processes.

Key words: Dividing wall batch distillation column, Middle-vessel, Temperature control, Neural network soft-sensor, Fuzzy control

摘要： Dividing wall batch distillation with middle vessel (DWBDM) is a new type of batch distillation column, with outstanding advantages of low capital cost, energy saving and flexible operation. However, temperature control of DWBDM process is challenging, since inherently dynamic and highly nonlinear, which make it difficult to give the controller reasonable set value or optimal temperature profile for temperature control scheme. To overcome this obstacle, this study proposes a new strategy to develop temperature control scheme for DWBDM combining neural network soft-sensor with fuzzy control. Dynamic model of DWBDM was firstly developed and numerically solved by Python, with three control schemes: composition control by PID and fuzzy control respectively, and temperature control by fuzzy control with neural network soft-sensor. For dynamic process, the neural networks with memory functions, such as RNN, LSTM and GRU, are used to handle with time-series data. The results from a case example show that the new control scheme can perform a good temperature control of DWBDM with the same or even better product purities as traditional PID or fuzzy control, and fuzzy control could reduce the effect of prediction error from neural network, indicating that it is a highly feasible and effective control approach for DWBDM, and could even be extended to other dynamic processes.

关键词: Dividing wall batch distillation column, Middle-vessel, Temperature control, Neural network soft-sensor, Fuzzy control