Design and control of a middle-vessel batch distillation for separating DMC–EMC–DEC mixture

In this paper, the mixture of dimethyl carbonate, ethyl methyl carbonate and diethyl carbonate was separated by middle-vessel batch distillation with feeding in middle-vessel and process control characteristics were researched. The steady state simulation results in Aspen Plus were exported to Aspen Dynamics. Then control effect of liquid level control with HighSelector, composition control(structure1, structure2) and temperature control(proportional action, proportional integration action) were proposed. Composition control structure 2 and temperature control with PI action were investigated to achieve a good control effect.

Composition control and temperature inferential control of dividing wall column based on model predictive control and PI strategies

The dividing wall column(DWC) is considered as a major breakthrough in distillation technology and has good prospect of industrialization. Model predictive control(MPC) is an advanced control strategy that has acquired extensive applications in various industries. In this study, MPC is applied to the process for separating ethanol,n-propanol, and n-butanol ternary mixture in a fully thermally coupled DWC. Both composition control and temperature inferential control are considered. The multiobjective genetic algorithm function "gamultiobj" in Matlab is used for the weight tuning of MPC. Comparisons are made between the control performances of MPC and PI strategies. Simulation results show that although both MPC and PI schemes can stabilize the DWC in case of feed disturbances, MPC generally behaves better than the PI strategy for both composition control and temperature inferential control, resulting in a more stable and superior performance with lower values of integral of squared error(ISE).

A Learning Evasive Email-Based P2P-Like Botnet

Nowadays, machine learning is widely used in malware detection system as a core component. The machine learning algorithm is designed under the assumption that all datasets follow the same underlying data distribution. But the real-world malware data distribution is not stable and changes with time. By exploiting the knowledge of the machine learning algorithm and malware data concept drift problem, we show a novel learning evasive botnet architecture and a stealthy and secure C&C mechanism. Based on the email communication channel, we construct a stealthy email-based P2 P-like botnet that exploit the excellent reputation of email servers and a huge amount of benign email communication in the same channel. The experiment results show horizontal correlation learning algorithm is difficult to separate malicious email traffic from normal email traffic based on the volume features and time-related features with enough confidence. We discuss the malware data concept drift and possible defense strategies.