Over the last decade, one of the most significant areas under focus in process safety management was developing an inherently safer design. The main objective of having an inherently safer design is to avoid hazards and risks from developing in the first place, rather than to reduce them after they have already occurred. A number of strategies, including index-based and other types, are used in today's process industries. This paper provides a brief overview of the current inherent design methods used in the process industries. This study also details how new technologies such as fuzzy logic and machine learning are used in the improvement of inherently safer designs. Traditional safety evaluation methods have flaws such as poor accuracy, large human element influence, which can affect the degree of safety. Inherently safer design prediction was modeled using various machine learning techniques like random forest, support vector machine (SVM), and K-neighborhood algorithm. Accuracy obtained for the sample prediction of upper flammability limit while using random forest algorithm was found to be more efficient while comparing with K-neighborhood and support vector machine algorithms. Accuracy obtained was in the range of 90%–95% for each epoch. The accuracy of the model will always be dependent on the type of parameters that we select for prediction. By considering more safety parameters and efficient machine learning algorithms for training models, we can develop systems with high accuracy predictions for inherently safer process plants.

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模糊逻辑和机器学习技术在过程安全管理中提高内在安全设计的应用：简要研究

在过去十年中，过程安全管理中最受关注的领域之一是开发本质上更安全的设计。拥有本质上更安全的设计的主要目标是从一开始就避免危险和风险的发展，而不是在它们已经发生后减少它们。在当今的流程工业中使用了许多策略，包括基于索引的策略和其他类型。本文简要概述了过程工业中当前使用的固有设计方法。这项研究还详细介绍了如何使用模糊逻辑和机器学习等新技术来改进本质上更安全的设计。传统的安全评价方法存在准确性差、人为因素影响大等缺陷，会影响安全程度。使用随机森林、支持向量机 (SVM) 和 K 邻域算法等各种机器学习技术对本质上更安全的设计预测进行建模。与 K 邻域和支持向量机算法相比，使用随机森林算法获得的样本预测可燃性上限的准确性被发现更有效。每个时期获得的准确度在 90%–95% 的范围内。模型的准确性始终取决于我们为预测选择的参数类型。通过为训练模型考虑更多的安全参数和有效的机器学习算法，我们可以开发具有高精度预测的系统，从而实现本质上更安全的过程工厂。和 K 邻域算法。与 K 邻域和支持向量机算法相比，使用随机森林算法获得的样本预测可燃性上限的准确性被发现更有效。每个时期获得的准确度在 90%–95% 的范围内。模型的准确性始终取决于我们为预测选择的参数类型。通过为训练模型考虑更多的安全参数和有效的机器学习算法，我们可以开发具有高精度预测的系统，从而实现本质上更安全的过程工厂。和 K 邻域算法。与 K 邻域和支持向量机算法相比，使用随机森林算法获得的样本预测可燃性上限的准确性被发现更有效。每个时期获得的准确度在 90%–95% 的范围内。模型的准确性始终取决于我们为预测选择的参数类型。通过为训练模型考虑更多的安全参数和有效的机器学习算法，我们可以开发具有高精度预测的系统，从而实现本质上更安全的过程工厂。每个时期获得的准确度在 90%–95% 的范围内。模型的准确性始终取决于我们为预测选择的参数类型。通过为训练模型考虑更多的安全参数和有效的机器学习算法，我们可以开发具有高精度预测的系统，从而实现本质上更安全的过程工厂。每个时期获得的准确度在 90%–95% 的范围内。模型的准确性始终取决于我们为预测选择的参数类型。通过为训练模型考虑更多的安全参数和有效的机器学习算法，我们可以开发具有高精度预测的系统，从而实现本质上更安全的过程工厂。