Non-thermal microplasma is a promising technology for efficient ozone generation for water sterilization. However, there remains a niche to improve the energy efficiency of the production process. The studies investigating the combined effects of interacting parameters affecting ozone generation are scarce. Studying more than one parameter is a limitation using standard experimental protocols. However, modeling tools such as Response Surface Methodology and Artificial Neural Network provides an opportunity to study the interaction between parameters and propose a mathematical model to predict ozone concentration under various experimental conditions. A robust model providing an insight into parametric interaction and better forecasting can reduce the required power requirement making it cleaner and sustainable. In this study, a Dielectric Barrier Discharge-Corona hybrid plasma microreactor, combining the homogeneity of the former and high energy streamers of the latter, was used to investigate factors affecting ozone generation. Response Surface Methodology was used with Central Composite Design for experimental design. A model was developed for analyzing the correlation of parameters, evaluate complex interactions among independent factors and optimization. Artificial Neural Network model based on Feed-Forward Backpropagation Network was developed to predict the response. The results were compared with the mathematical models developed by Response Surface Methodology. To the best of our knowledge, a study on ozone generation and optimization in a Dielectric Barrier Discharge-Corona hybrid discharge reactor do not exist. Similarly, such a detailed analysis and comparison of Response Surface Methodology and Artificial Neural Network for ozone generation is reported for the first time. Ozone generation was favored at lower values of flow rate and pressure of air, frequency, and higher voltage and electrode length. Response Surface Methodology was found to have a lower value of R² = 0.9348 as compared to Artificial Neural Network, i.e., R² = 0.9965. Root Mean Square Error obtained from Response Surface Methodology (5.0737) is approximately four times higher as compared to Artificial Neural Network (1.1779). The results showed the Artificial Neural Network model is more reliable than the Response Surface Methodology to study the interacting parameters and prediction. The model could be related to the real-time experiments to predict the ozone concentration under various experimental conditions and make the sterilization process cleaner.

非热微等离子体是用于水消毒的有效臭氧产生的有前途的技术。但是，在提高生产过程的能源效率方面仍然存在利基。缺乏研究相互作用参数影响臭氧产生的综合影响的研究。使用标准实验方案研究多个参数是一个局限。但是，诸如响应面方法和人工神经网络等建模工具为研究参数之间的相互作用提供了机会，并提出了数学模型来预测各种实验条件下的臭氧浓度。强大的模型可提供对参数交互作用的深入了解和更好的预测，可以减少所需的电源需求，使其更清洁，更可持续。在这项研究中，一个介电阻挡放电-电晕混合等离子体微反应器，结合了前者和后者的高能流光，研究了影响臭氧产生的因素。响应面方法与中央复合设计一起用于实验设计。开发了一个模型，用于分析参数的相关性，评估独立因素之间的复杂相互作用以及优化。建立了基于前馈反向传播网络的人工神经网络模型来预测响应。将结果与响应曲面方法学开发的数学模型进行了比较。据我们所知，不存在有关介电阻挡放电-电晕混合放电反应器中臭氧产生和优化的研究。相似地，首次报道了这种响应面方法和人工神经网络用于臭氧生成的详细分析和比较。在较低的空气流速和压力值，频率以及较高的电压和电极长度的情况下，有利于臭氧的产生。发现响应面方法论的R值较低²  = 0.9348相比，人工神经网络，即，R ²  = 0.9965。从响应表面方法学（5.0737）获得的均方根误差是人工神经网络（1.1779）的大约四倍。结果表明，人工神经网络模型比响应面方法学更可靠地研究相互作用参数和预测。该模型可以与实时实验相关，以预测各种实验条件下的臭氧浓度，并使消毒过程更清洁。