Abstract Response surface analysis has become a standard for characterization of extrusion experiments in recent years. While response surface experiments provide large amounts of useful data, the problem persists in how data can be used to successfully design specified products for a consumer. The use of genetic algorithms was explored as a potential tool that can help solve response surface data to identify extrusion conditions needed for desired product design. Response surface regression was conducted on five varieties of peas and the regression equations were used to create a way of measuring fitness in a genetic algorithm model routine. In doing so, extrusion conditions of screw speed and temperature for were successfully predicted for response factors (radial expansion, density, WAI, WSI, pressure, motor torque, SME, and color) of all the pea varieties with strong fitness (>0.90). Results suggest that optimization using genetic algorithms can have a beneficial impact selecting extrusion conditions.

中文翻译：

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通过回归建模和遗传算法优化双螺杆食品挤压加工

摘要 近年来，响应面分析已成为表征挤压实验的标准。虽然响应面实验提供了大量有用的数据，但问题仍然存在于如何使用数据为消费者成功设计指定产品。探索使用遗传算法作为一种潜在工具，可以帮助解决响应面数据，以确定所需产品设计所需的挤出条件。对五种豌豆进行了响应面回归，并使用回归方程创建了一种在遗传算法模型例程中测量适合度的方法。在此过程中，成功预测了螺杆速度和温度的挤出条件的响应因素（径向膨胀、密度、WAI、WSI、压力、电机扭矩、SME、和颜色）所有具有强适应度（>0.90）的豌豆品种。结果表明，使用遗传算法进行优化可以对选择挤出条件产生有益的影响。