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. Case studies, such as successful implementations with cotton biomass byproducts, illustrate furfural's practical applications and environmental benefits. The study underscores the need for ongoing research, supportive policies, and furfural's growing role in sustainable agriculture and industry. It is focused on furfural's essential contribution to promoting environmental stewardship and sustainable practices. By examining furfural's role as a value-added product from agricultural residues, this review provides insights into its economic viability and potential challenges.

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. Therefore, it can be inferred that the gaussian process and support vector machine methods can be used to predict the respective outputs, i.e., flexural and compressive strength. The Gaussian process and Support vector machines Stochastic predicts better outcomes for flexural and compressive strength because it has a higher coefficient of correlation (0.8235 and 0.9462), lower mean absolute and root mean squared error values as (2.2808 and 1.8104) and (2.8527 and 2.3430), respectively. Results suggest that all applied techniques are reliable for predicting the compressive and flexural strength of concrete and are able to reduce the experimental work time. In comparison to input factors for this data set, the number of curing days followed by the CA, C, FA, w, and MP is essential in predicting the flexural and compressive strength of a concrete mix for this data set.