In this paper, a finite control set model-free predictive current control (FCS-MFPCC) of a permanent magnet synchronous motor is presented. The control scheme addresses the problems of large current fluctuation and decline of the motor system performance during parameter perturbation for the traditional finite control set model predictive current control (FCS-MPCC). Firstly, the mathematical model of the motor is analyzed and derived during parameter perturbation, and a new hyperlocal model of the motor is established based on this mathematical model. Secondly, a finite control set model-free predictive current controller is designed based on the new hyperlocal model, and a current error correction factor is introduced to correct the prediction error. Meanwhile, the stability of the observer is demonstrated via the Lyapunov theory. The simulation results show that the proposed control strategy reduces current fluctuation compared with the FCS-MPCC strategy, and the system is robust during parameter perturbation.

Grinding is widely used in mechanical manufacturing to obtain both precision and part requirements. In order to achieve carbon efficiency improvement and save costs, carbon emission and processing cost models of the grinding process are established in this study. In the modeling process, a speed-change-based adjustment function was introduced to dynamically derive the change of the target model. The carbon emission model was derived from the grinding force using regression. Considering the constraints of machine tool equipment performance and processing quality requirements, the grinding wheel’s linear velocity, cutting feed rate, and the rotation speed of the workpiece were selected as the optimization variables, and the improved NSGA-II algorithm was applied to solve the optimization model. Finally, fuzzy matter element analysis was used to evaluate the most optimal processing plan.

Selective catalytic reduction (SCR) technology is the main means of reducing NO_X emissions. Urea injection structure parameters (i.e., the hole number in a nozzle, spray angle, the distance between the nozzle and the front section of the catalyst) and operating parameters (i.e., exhaust gas temperature, gas hourly space velocity, ammonia-to-nitrogen ratio (ANR)) of the SCR systems have a significant impact on denitrification efficiency (deNO_X). A three-dimensional simulation model is developed to investigate the impact of diverse parameters on deNO_X. The influence of temperature, ANR and space velocity on the deNO_X of SCR system is studied by using the control variable method. The simulation model is modified according to the SCR catalyst gas distribution test results. A diesel bench experiment is conducted to test the deNO_X of the SCR system of D2 cycle and two additional operating points to verify the accuracy of the numerical simulation model. Based on this model, the influence of key factors on the deNO_X of SCR system is studied. In SCR system, the optimal urea injection angle is about 75°, the optimal number of nozzle holes is 4, the distance between the optimal urea injection point and the front section of the catalyst is 8 times of the pipe diameter, and the optimal operating temperature is 300 ∼ 420℃. The influence trend of ANR on deNO_X at different temperatures is obtained. This provides a theoretical support for the realization of SCR system in the future.

In order to solve the problems of flexible process route and workshop scheduling scheme changes frequently in the multi-variety small batch production mode, a multiprocess route scheduling optimization model with carbon emissions and cost as the multi-objective was established. At the same time, it is considered to optimize under the existing machine tool conditions in the workshop, then the theory of logistics intensity between equipment is introduced into the model. By designing efficient constraints to ensure reasonable processing logic, and then applying multilayer coding genetic algorithm to solve the case. The optimization results under single-target and multi-target conditions are contrasted and analyzed, so as to guide enterprises to choose a reasonable scheduling plan, improve the carbon efficiency of the production line, and save costs.