System Analysis and Simulation of the Process of Destructive Hydrogenization of Deasphaltisate of Heavy Oil

The article presents a systematic analysis and simulation of the process of destructive hydrogenation of deasphalted oil. The process of thermoregeneration of spent zeolite and the surface - acid properties of CaA zeolite catalysts are also studied. It has been established that such patterns that allow predicting their influence and to regulate the quality of the hydrogenation obtained on one or another form of the catalyst obtained from the studied factors and catalysts. In addition, the obtained data can be used in the search for the optimal process modes of the process under consideration on the specific form of the catalyst.

Optimization of Process Parameters for Enhanced Degradation of Methylene Blue by Trough Ultrasonic

Due to the complex quality and the large discharge of printing and dyeing wastewater, it will pollute the environment and affect human health. Therefore, how to use efficient and inexpensive treatment methods to treat printing and dyeing wastewater has become an urgent problem to be solved. At present, most printing and dyeing wastewater contains methylene blue pollutants. Based on the previous research in this article, the process conditions for the enhanced degradation of methylene blue by trough ultrasound are optimized. Orthogonal test results show that the optimal process parameter for the degradation of methylene blue by trough ultrasonic is pH 12.70, and the initial With a concentration of 10.00mg/L and an ultrasonic power of 200W, under the above optimal process conditions, the degradation rate of methylene blue is 77.95%; Ultrasound improves the rapid degradation of methylene blue through mechanisms such as cavitation, thermal and mechanical effects. This process can be used for the industrial degradation of methylene blue. The application provides a research basis.

Optimization of injection molding process parameters based on GA-ELM-GA

The most common optimization method for the optimization of injection mold process parameters is range analysis, but there is often a nonlinear coupling relationship between injection molding process parameters and quality indicators. Therefore, it is difficult to find the optimal process combination in range analysis. In this article, a genetic algorithm optimized extreme learning machine network model (GA-ELM) combined with genetic algorithm (GA) was proposed to optimize the process parameters of the injection mold. Take the injection molding process parameter optimization of an electrical appliance buckle cover shell as an example. In order to find the process parameters corresponding to the minimum warpage deformation, an orthogonal experiment was designed and the results of the orthogonal experiment were analyzed. Then, the corresponding optimal process combination and the degree of influence of process parameters on the warpage deformation were obtained. At the same time, the extreme learning machine network model (GA-ELM) optimized by the genetic algorithm was used to predict the warpage deformation of the plastic part. The trained GA-ELM model can map non-linear coupling relationship between the five process parameters and the warpage deformation well. And the optimal process parameters in the trained GA-ELM network model was searched by the powerful optimization ability of genetic algorithm. Generally speaking, the warpage deformation after optimization by range analysis is reduced by 6.7% compared with the minimum warpage after optimization by orthogonal experiment. But compared to the minimum warpage deformation after orthogonal experiment optimization, the warpage deformation after GAELM-GA optimization is reduced by 22%, which is better than that of the range analysis, thus verifying the feasibility and the optimization of the optimization method. This optimization method provides a certain theoretical reference and technical support for the field involving the optimization of process parameters.

Prediction for Dilution Rate of AlCoCrFeNi Coatings by Laser Cladding Based on a BP Neural Network

The dilution rate has a significant impact on the composition and microstructure of the coatings, and the dilution rate and process parameters have a complex coupling relationship. In this study, three process parameters, namely laser power, powder feeding rate, and scanning speed, were selected as variables to design the orthogonal experiment. The dilution rate and hardness data were obtained from AlCoCrFeNi coatings based on orthogonal experiments. Then, a BP neural network was used to establish a prediction model of the process parameters on the dilution rate. The established BP neural network exhibited good prediction of the dilution rate of AlCoCrFeNi coatings, and the average relative error between the predicted value and the experimental value was only 5.89%. Subsequently, the AlCoCrFeNi coating was fabricated with the optimal process parameters. The results show that the coating was well-formed without defects, such as cracks and pores. The microhardness of the AlCoCrFeNi coating prepared with the optimal process parameters was 521.6 HV0.3. The elements were uniformly distributed in the microstructure, and the grain size was about 20–60 μm. The microstructure of the AlCoCrFeNi coating was only composed of the BCC phase without the existence of the FCC phase and intermetallic compounds.

Optimization of CNC CO2 Laser Cutting Process Parameters on Acrylic Cutting Using Taguchi Grey Relational Analysis and Response Surface Methodology

Polymethylmethacrylate (acrylic) has some important characteristics such as light weight, impact-resistant, and high durability. In a manufacturing industry, acrylic has been widely used as the basic material for billboard products, decorative lights, canopies, and room decorations. This research aims at determining the optimal process parameters of the laser cutting process. The experiment was conducted using multi-response Taguchi method involving four responses, namely processing time, dimensional accuracy, surface roughness, and carbon emissions. The Taguchi method is used to determine the Signal to Noise (SNR) for each response. The Grey Relational Analysis (GRA) method is performed by calculating the normalized weight of SNR for each response to determine the optimal setting level of each factor applied in the experiment. The Response Surface Methodology (RSM) was applied to determine the mathematical model based on the results of the experiment to allow the multi-objective optimization and determine the exact value of optimal process parameters which simultaneously compromise all the responses. Based on the results of the experiment, the optimal process parameters are 65% of the laser power, 4 mm/s of the cutting speed, and 4 mm of nozzle distance. Whereas from the results multi-objective optimization, the optimal process parameters are 75% of laser power, 5.9 mm/s of cutting speed, and 3mm of nozzle distance.

Investigation rectifier circuits based on the mathematical models in the two-dimensional space

The mathematical model for the rectifier circuit using semiconductor diodes is setup in this paper. The properties of the rectifier circuit presented by the ordinary differential equation containing a control parameter K. When K is large enough, the studied equation gives a trajectory approximating to a trajectory of the rectifier circuit above. The theorem about the approximation of these solutions with arbitrary small error (this error can be controlled by increasing K). The usefulness of this model is illustrated via concrete example. This study can to get more profound results in further and investigate an optimal process for an assembly line of rectifiers in electrical engineering.