A neural network model based on a chaotic particle swarm optimization (CPSO) radial basis function-back propagation (RBF-BP) neural network was suggested to improve the accuracy of reactor temperature prediction. The training efficiency of the RBF-BP neural network is influenced to some degree by the large randomness of the initial weight and threshold. To address the impact of initial weight and threshold uncertainty on the training efficiency of the RBF-BP combined neural network, this paper proposes using a chaotic particle swarm optimization algorithm to correct the RBF-BP neural network’s initial weight and threshold, as well as to optimize the RBF-BP neural network to speed up the algorithm and improve prediction accuracy. The measured temperature of the reactor acquired by on-site enterprises was confirmed and compared to the predicted results of the BP, RBF-BP, and PSO-RBF-BP neural network models. Finally, Matlab simulation tests were performed, and the experimental data revealed that the CPSO-RBF-BP combined neural network model suggested in this paper had a root-mean-square error of 17.3%, an average absolute error of 11.4%, and a fitting value of 99.791%. Prediction accuracy and efficiency were superior to those of the BP, RBF-BP, and PSO-RBF-BP models. The suggested model’s validity and feasibility were established. The study findings may provide some reference values for the reactor’s temperature prediction.

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In the chemical industry, a reactor is an absolutely necessary container. The fact that its dynamic qualities are nonlinear and unknown, however, is what causes the temperature to deviate from the value that was specified. As a result, the typical PID control cannot fulfill the prerequisites of the production process. A new nonlinear function is presented to replace the function that was previously used, and a temperature controller that is based on better fractional order active disturbance rejection is devised. On the basis of a new fractional order temperature detector (FOTD), a new fractional order equilibrium state observer (FOESO), and nonlinear function, an improved fractional order active disturbance rejection controller has been developed. A model of the reactor was created, and the dynamic properties of temperature control were investigated. By simulation and experimentation, it was demonstrated that the strategy has a number of benefits and is effective. In this approach, the information provided by the model is exploited to its maximum potential, and the temperature of the inlet cooling water is employed as the temperature control disturbance for feedforward compensation. Over the entirety of the process, this guarantees that the desired temperature will be preserved. When compared to FADRC, PID, and ADRC, the rising time is increased by 5 s, and the overshoot is raised by 25%. It has been established that the fraction-order active disturbance rejection controller has a quicker response speed, a higher capacity for anti-interference, and a quicker speed of stabilization.

A cognitive-based routing algorithm is proposed. Concepts like local form and path algorithms are developed. Unlike current mainstream routing algorithms assume that all people know everything about the environment, the proposed algorithm allows people to have a complete or incomplete map knowledge and built up their own map knowledge in a piecemeal fashion. Using a hospital floor plan as the scenario, numerical experiments are conducted by assuming pedestrians to have different levels of map knowledge. Results show that reasonable routes could be frequently found even if pedestrians only have an incomplete knowledge of the network. Also pedestrians generally need to traverse more rooms if having zero or less map knowledge. Hence the proposed algorithm’s effectiveness is validated to some extent.

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