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Oxide concentration at reactor inlet is one of the most important factor effecting the quality of ethylene oxide concentration. A control method with adaptive PID of single neuron is propose using the parameter self-learning with adaptive PID controller of single neuron for the oxide flow control system. The simulation results show that this design scheme has a better dynamic performance than traditional PID scheme to verify the feasibility of this method.

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For the quality control of developed small batch manufacturing mode, a method based on stochastic weighted theory is proposed to optimize control parameters of control charts. The small sample data is sampled again by stochastic weighted method to obtain more information. Then, the sample data’s distribution parameters are estimated. After the estimated distribution parameter are tested, control parameters of control charts could be optimized dynamically. The method improves deficiencies of traditional control charts under low-volume production and realizes the quality control of small batch manufacturing. Experiments show that the optimized control parameters approach theory true values and are better than the traditional control charts’ control parameters. Therefore, the proposed method is fit for the quality control of small batch manufacturing.