Nano Membrane Aerobic Fermentation Composting Control System

With the help of microorganisms,the organic matter of livestock and poultry manure in the closed environment covered with film can be transformed into decomposed fertilizer through high-temperature fermentation,which can effectively improve the environment and supplement the nutrients and organic matter of the soil.Automation of the control system of nano-film aerobic fermentation composting is an important research field of precise composting,effective utilization of organic solid wastes and efficient decomposition.Aiming at the problems existing in the pressure control of the existing composting system,based on the system technology and its modeling,this paper developed a micro-positive pressure monitoring device for organic film-covered aerobic composting,and proposed a particle swarm optimization BP neural network PID control method which improved the widely used incremental PID algorithm by using simulated annealing algorithm.The effect of optimization at all levels was confirmed by MATLAB simulation,and the control system hardware and software of SCL language were used to further optimize the Io T control system of nano-film aerobic composting.The simulation results show that the temperature of the controlled reactor is a second-order time-delay link which is affected by the environmental temperature and airspace,and the pressure is a first-order time-delay link which is affected by the oxygen concentration.With the increase of learning rate parameters of neural network in a certain range,the prediction accuracy of BP neural network will be enhanced.The test function shows that the optimization performance of the improved particle swarm optimization algorithm can greatly reduce the number of local minima,and the ability to escape from the black hole interval is very good.The improved particle swarm optimization algorithm improves the efficiency and stability of the algorithm to at least 160 steps and 0.05.The population has an important influence on the improved particle swarm optimization algorithm.With the increase of population,the decreasing range of algorithm efficiency slows down and the increasing range of algorithm stability slows down.The experimental results show that the improvement ability of particle swarm optimization algorithm is slightly affected by the logic cycle of PLC.Because of the influence of PLC performance,composting environment temperature,PH value and other system errors,the error between experimental optimization efficiency and simulation results is within a reasonable range,which proves the feasibility of the algorithm and the correctness of the simulation results.A new control method of the Internet of Things for composting is developed.In the HMI interface of field human-computer interaction,various display and setting functions are displayed through remote control of mobile phones,and the experimental and running results on the webpage can be observed in real time.