Harmful gases determination and temperature intelligent control algorithm in piggery

By adequately utilizing spectrum features during the reaction of gas molecules from sensor output signals, and using a method combining wavelet transform with genetic algorithm for harmful gases determination in piggery, the sensor cross-sensitivity problem from NH3 and H2S sensors was solved, and the effectiveness of determination of harmful gases was improved. Experimental results showed that accuracy of qualitative determination by BP neural network with the proposed method reached 92%, and average accuracy of quantitative determination reached 87%. A fuzzy control algorithm was designed for intelligent temperature control in piggery, and the Matlab simulation results show that system response time becomes shorter and steady-state error is lower by this control algorithm, resulting in satisfaction for requirement of piggery temperature control.