Metal-Organic Framework Synthesis System Based on Fuzzy Predictive Control via Network Transmission

The purpose of this study is to construct metal-organic framework (MOF) synthesis heating systems based on fuzzy method for monitoring and automatic control. In this study, the temperature sensing module for measurements sensed values that it through a wireless ZigBee chips and wired DAQ device for real-time data transmission. Because MOF synthesis, often due to different modes of heating or heating instability caused by its nucleation and crystal growth rate, is an important influence, leading to different crystallinity, the use of fuzzy theory to predict the temperature parameter and instant heating MOF synthesis parameters can be adjusted to improve the accuracy of the system. The research system to RS-232 interface module for infrared emission control packets issued and automated control of the furnace through the infrared receiver module. This study is based on a terminal interface window of Visual Basic programming and LabView graphical diagram for control system design. Finally, this research, through a number of experiments to validate the use of fuzzy system development methods and networks, can improve the accuracy of the reaction efficiency MOF sensing and control the heating system

On designing optimal control systems through genetic and neuro-fuzzy techniques

Many industrial processes are affected by flow disturbances and sensor noise. To maintain optimal timing performances, the control system needs to adapt continuously to these changes. The goodness of a control system depends on timing parameters such as settling time, rise time and overshoot. Avoiding undesirable overshoot, longer settling times and vibration from a state to another one, the designed control system gives optimal control performances. Control problems can be overcome using computational intelligence procedures. The target of this work is to find optimal combinations of intelligent techniques such as fuzzy logic, Genetic Algorithms and neural networks to obtain good control performances. The membership functions of the designed fuzzy controllers are optimized through Genetic Algorithms. Moreover, the fuzzy rules weights are tuned both Genetic Algorithms and neural networks. In this way, the control system has the capability to learn from data. The results show that our controllers improve the timing performances of conventional controllers. Moreover, the fuzzy rules weights optimization with Genetic Algorithms is improved using neural networks techniques which suitably tune the weights. © 2011 IEEE