Furfural as a low-volume, high-value asset from agricultural residues: A review on production, agricultural applications and environmental sustainability.

This comprehensive review explores furfural production from agricultural residues, focusing on its significance as a low-volume, high-value asset crucial for environmental sustainability. It covers diverse production technologies, recent advancements, and applications in agriculture, evaluating furfural's potential to enhance crop resilience and yield. Showing its role in a circular economy, the review discusses how furfural can replace conventional petrochemical processes, thereby reducing environmental impact.

Innovations in textile wastewater management: a review of zero liquid discharge technology.

Zero liquid discharge (ZLD) technology emerges as a transformative solution for sustainable wastewater management in the textile industry, emphasizing water recycling and discharge minimization. This review comprehensively explores ZLD's pivotal role in reshaping wastewater management practices within the textile sector. With a primary focus on water recycling and minimized discharge, the review thoroughly examines the economic and environmental dimensions of ZLD.

Decision Tree-Based Modeling of the Aeration Effectiveness of Circular Plunging Jets.

Since soft computing has gained a lot of attention in hydrological studies, this study focuses on predicting aeration efficiency () using circular plunging jets employing soft computing techniques such as reduced error pruning tree (REPTree), random forest (RF), and M5P. The study undertaken required the development and validation of models, which were achieved using 63 experimental data values with input variables, such as angle of inclination of tilt channel (α), number of plunging jets (), discharge of each jet (), hydraulic radius of each jet (HR), and Froude number (Fr. No), to evaluate the aeration efficiency (), which served as the output variable.

Analytical Investigation of the Impact of Jet Geometry on Aeration Effectiveness Using Soft Computing Techniques.

Jet aeration is a commonly used technique for introducing air into water during wastewater treatment. In this investigation, the efficacy of different soft computing models, namely, Random Forest, Reduced Error Pruning Tree, Artificial Neural Network (ANN), Gaussian Process, and Support Vector Machine, was examined in predicting the aeration efficiency (E) of circular and square jet configurations in an open channel flow. A total of 126 experimental data points were utilized to develop and validate these models.

Marshall Stability Prediction with Glass and Carbon Fiber Modified Asphalt Mix Using Machine Learning Techniques.

Pavement design is a long-term structural analysis that is required to distribute traffic loads throughout all road levels. To construct roads for rising traffic volumes while preserving natural resources and materials, a better knowledge of road paving materials is required. The current study focused on the prediction of Marshall stability of asphalt mixes constituted of glass, carbon, and glass-carbon combination fibers to exploit the best potential of the hybrid asphalt mix by applying five machine learning models, i.

Machine Learning Techniques for Evaluating Concrete Strength with Waste Marble Powder.

The purpose of the research is to predict the compressive and flexural strengths of the concrete mix by using waste marble powder as a partial replacement of cement and sand, based on the experimental data that was acquired from the laboratory tests. In order to accomplish the goal, the models of Support vector machines, Support vector machines with bagging and Stochastic, Linear regression, and Gaussian processes were applied to the experimental data for predicting the compressive and flexural strength of concrete. The effectiveness of models was also evaluated by using statistical criteria.