Artificial neural networks are revolutionizing the field of engineering because of their ability to model complex non-linear problems without explicit programming. Their applications in different areas, such as manufacturing and healthcare, have provided a new path away from traditional modeling techniques. Nonetheless, users of this technology, especially those without prior knowledge of neural networks, spend a considerable amount of time gaining a basic understanding of the use of technology. Traditional trial-and-error approaches are often employed in training these neural networks, which further increases the time spent in developing them. Owing to the laborious nature of the trial-and-error method, the optimal or best hyperparameters of a particular neural network may not be determined, thereby affecting the accuracy of the developed model. Hence, in this study, a software platform is presented to aid in the training and development of neural networks by using genetic algorithms (GAs) for optimizing the model's hyperparameters, such as the number of neurons, learning rate, and activation function. As an essential aspect of chemical engineering processes, design or operating-parameter optimization is also included in this software package, wherein the best (optimized) weights and biases from the neural network are saved and employed in another GA to optimize key process variables, such as temperature, and velocity, as required by the user. This dual-purpose provides a complete application of neural networks that are primarily encountered in many engineering disciplines. The software platform can also plot 3D contours, heat maps (correlation plots), and other line graphs. For the validation and generalization of the software, it was benchmarked against five cases presented by different authors across various chemical engineering fields. The prediction results obtained using the software package were higher than those presented in the published literature, demonstrating the superior performance of the software package.

人工神经网络正在彻底改变工程领域，因为它们能够在没有显式编程的情况下对复杂的非线性问题进行建模。它们在制造和医疗保健等不同领域的应用提供了一条摆脱传统建模技术的新途径。尽管如此，这项技术的用户，尤其是那些没有神经网络先验知识的用户，会花费大量时间来获得对技术使用的基本了解。传统的试错方法经常用于训练这些神经网络，这进一步增加了开发它们所花费的时间。由于试错法的费力性质，可能无法确定特定神经网络的最佳或最佳超参数，从而影响开发模型的准确性。因此，在本研究中，提出了一个软件平台，通过使用遗传算法 (GA) 来优化模型的超参数，例如神经元数量、学习率和激活函数，来帮助训练和开发神经网络。作为化学工程过程的一个重要方面，设计或操作参数优化也包含在这个软件包中，其中来自神经网络的最佳（优化）权重和偏差被保存并用于另一个 GA 以优化关键过程变量，例如作为温度和速度，根据用户的要求。这种双重用途提供了主要在许多工程学科中遇到的神经网络的完整应用。该软件平台还可以绘制 3D 轮廓、热图（相关图）和其他折线图。为了验证和推广该软件，它针对不同化学工程领域的不同作者提出的五个案例进行了基准测试。使用该软件包获得的预测结果高于已发表文献中的预测结果，证明了该软件包的优越性能。