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首页> 外文OA文献 >A comparative study between Neural Networks (NN)-based and adaptive-PID controllers for the optimal bio-hydrogen gas production in microbial electrolysis cell reactor
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A comparative study between Neural Networks (NN)-based and adaptive-PID controllers for the optimal bio-hydrogen gas production in microbial electrolysis cell reactor

机译：基于神经网络和自适应PID控制器的微生物电解池反应器中最佳生物氢气产生量的比较研究

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The main challenge of the hydrogen production study for the MEC reactor is to obtain a good automatic control system due to the nonlinearity and complexity of the microbial interactions. To address this issue an integrated approach involving process modeling, optimization and advanced control has to be implemented. This work focus on theudcontroller’s performance in the control system; neural network (NN)-based and Adaptive-PID controllers. The study has been carried out under optimal condition for the production of bio-hydrogen gas wherein the controller output are based on the correlation of the optimal current and voltage to the MEC. A Ziegler–Nichols tuning method and an adaptive gain technique have been used to design the PID controller, while the neural network controller has been designed from the inverse response of the MEC neural network model.
机译：由于微生物相互作用的非线性和复杂性，MEC反应器制氢研究的主要挑战是获得一个良好的自动控制系统。为了解决这个问题，必须采用涉及过程建模，优化和高级控制的集成方法。这项工作着重于 udcontroller在控制系统中的性能；基于神经网络（NN）和Adaptive-PID控制器。该研究是在生产生物氢气的最佳条件下进行的，其中控制器的输出基于最佳电流和电压与MEC的相关性。使用Ziegler-Nichols调整方法和自适应增益技术来设计PID控制器，而根据MEC神经网络模型的逆响应来设计神经网络控制器。

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