One of the most essential operational difficulties that water companies face today is the capacity to manage their water treatment process daily. Companies are looking for long-term solutions to predict how their treatment methods may be enhanced as they face growing competition. Many models for biological growth rate control, such as the Monod and Contois models, have been suggested in the literature. This review further emphasized that the Contois model is the best and is more suited to predicting the performance of biological growth rate than the other applicable models with a high correlation coefficient. Furthermore, the most well-known models for optimizing and predicting the wastewater treatment process are response surface methodology (RSM) and artificial neural networks (ANN). Based on this review, the ANN is the best model for wastewater treatment with high accuracy in biological wastewater treatment. Furthermore, the present paper conducts a bibliometric analysis using VOSviewer to assess research performance and perform a scientific mapping of the most relevant literature in the field. A bibliometric study of the most recent publications in the SCOPUS database between 2018 and 2022 is performed to assess the top ten countries around the world in the publishing of employing these four models for wastewater treatment. Therefore, major contributors in the field include India, France, Iran, and China. Consequently, in this research, we propose a sustainable wastewater treatment model that uses the Contois model and the ANN model to save time and effort. This approach may be helpful in the design and operation of clean water treatment operations, as well as a tool for improving day-to-day performance management.

Regression testing is a widely used approach to confirm the correct functionality of the software in incremental development. The use of test cases makes it easier to test the ripple effect of changed requirements. Rigorous testing may help in meeting the quality criteria that is based on the conformance to the requirements as given by the intended stakeholders. However, a minimized and prioritized set of test cases may reduce the efforts and time required for testing while focusing on the timely delivery of the software application. In this research, a technique named TestReduce has been presented to get a minimal set of test cases based on high priority to ensure that the web application meets the required quality criteria. A new technique TestReduce is proposed with a blend of genetic algorithm to find an optimized and minimal set of test cases. The ultimate objective associated with this study is to provide a technique that may solve the minimization problem of regression test cases in the case of linked requirements. In this research, the 100-Dollar prioritization approach is used to define the priority of the new requirements.