Introduction

Zhou, Huamin

doi:10.1002/9781118444887.ch1

Cited by 3 publications

(1 citation statement)

References 143 publications

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“…Simulations and computer modeling, thanks to its usefulness, is helpful in many areas of science and industry [Zhou, 2013;Winsberg, 2015;Mityushev et al, 2018]. They enable foreseeable scenarios and situations that can impact the environment, thus allowing actions to be taken to avoid them [Luo et al, 2021].…”

Section: Introductionmentioning

confidence: 99%

Simulation of Increasing the Cell Membrane Permeability Under Steady-State Conditions

Trawczyńska

2024

J. Ecol. Eng.

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There is being presented simulation of increasing membrane permeability cellular baker's yeast for the set values concentrations of alcohol solutions. It allows for easy activity forecasting intracellular catalase. Due to the costs associated with disposal large volumes of waste and the purchase of permeabilizing agents the tolerance of the permeabilization process to changes in alcohols concentration was checked. For this purpose, appropriate mathematical models were used. The obtained results confirm the high sensitivity of the process to changes in concentration permeabilizing factor. Of the three permeabilizations studied, this is the process using ethanol turned out to be the least tolerant to change concentration of the permeabilizing factor. The concentration of the solution of this alcohol, which will be used in permeabilization to obtain cells yeast with catalase activity half of the optimal activity, is only 20% lower than the optimal concentration. Processes permeabilization using the other two alcohols considered less sensitive to changes in their concentration. The concentrations of 1-propanol and 2-propanol solutions for A S = ½ A opt are 2.5 times and 3.5 times lower than the optimal concentrations, respectively.

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Section: Introductionmentioning

confidence: 99%

Simulation of Increasing the Cell Membrane Permeability Under Steady-State Conditions

Trawczyńska

2024

J. Ecol. Eng.

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Intelligent injection molding: Parameters self‐learning optimization using iterative gradient‐approximation adaptive method

Dong

Zhao

Zheng³

et al. 2021

J of Applied Polymer Sci

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Intelligent injection molding consists of three aspects: intelligent parameter optimization, process monitoring and control. The optimal process parameters are critical to guarantee product quality. Injection molding is a typical batch process and has the property that previous runs can provide feedback to optimize subsequent runs. This study proposes a self-learning parameter optimization method named iterative gradient-approximation adaptive optimization (IGAO) method, which adopts the batch-to-batch information to remove the need to establish optimization model with large numbers of experiments. The analysis and the optimization of the parameters can be performed simultaneously. The IGAO method approximates the gradient iteratively and assigns an adaptive step size to each parameter according to gradient accumulation.The experiments conducted in both simulation software and injection molding machine prove that the method has fast convergence speed. Standard product weight can be obtained within 11 runs from three different starting process parameters. Experiment results show that 25% less steps are needed compared with the traditional gradient descent method. The method also has good stability to resist disturbances during the optimization procedure. In general, the proposed IGAO method is fast, stable and robust, and it has good prospects for parameter optimization in batch processes.

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A Study on the Temperature Optimization of Mold and Melt Using Design of Experiments for Children's Chair

Trung

2023

KEM

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The quality of the finished product is affected by a number of factors during the plastic injection molding process. Two crucial process variables in the creation of products are melted and mold temperature. The study uses the Design of Experiments tool in Autodesk Moldflow to look at the impact of melt and mold temperatures on injection molding technology. Analytical items are specifically made of polypropylene (PP) using kids' chair mold. According to simulation analysis results, there is a remarkable effect of the melt temperature on both time at end of packing as well as deflection in the range of the analytical temperature at tmold of [40, 80]°C and tmelt of [180, 220]°C. Melt temperature also shows a notable influence not only on deflection but also on sink mark depth and volumetric shrinkage, along with the criteria to evaluate the expense of a product (time at end of packing, total part weight).

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Introduction

Cited by 3 publications

References 143 publications

Simulation of Increasing the Cell Membrane Permeability Under Steady-State Conditions

Simulation of Increasing the Cell Membrane Permeability Under Steady-State Conditions

Intelligent injection molding: Parameters self‐learning optimization using iterative gradient‐approximation adaptive method

A Study on the Temperature Optimization of Mold and Melt Using Design of Experiments for Children's Chair

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