United Nations charter to build a sustainable future has paved the way for the introduction of the Sustainability Development Goals (SDGs) at a global forum. In particular, SDG 11 is aligned with the idea of developing cities and communities that provide quality human life, by attaining net-zero discharge and self-sustainability. In line with the efforts of the global community, biochar has emerged as a viable solution due to its ability to convert waste into value. Finding applications in a spectrum of domains, biochar is being studied for use as an adsorbent, a co-catalyst to promote industrial-grade reactions and as a feed for fuel cells. Moreover, the inclusion of biochar as a soil enhancement material advocates the implementation of closed-loop nutrient cycles. Hence, it is imperative to have a proper understanding of the biomass characteristics, the hydrothermal treatment and the process parameters to be adopted for the production of char in order to identify biomass feedstock based on the application. The current work provides insight into the key factors and conditions employed for the production of biochar based on the plethora of applications. In order build a basic framework to aid in the production of char, the development of a statistical correlation was undertaken to determine the feed and optimum process parameters for the production of biochar based on its applications.

The present study investigates the potential of using fecal ash as an adsorbent and demonstrates a self-sustaining, optimized approach for urea recovery from wastewater streams. Fecal ash was prepared by heating synthetic feces to 500 °C and then processing it as an adsorbent for urea adsorption from synthetic urine. Since this adsorption approach based on fecal ash is a promising alternative for wastewater treatment, it increases the process’ self- sustainability. Adsorption experiments with varying fecal ash loadings, initial urine concentrations, and adsorption temperatures were conducted, and the acquired data were applied to determine the adsorption kinetics. These three process parameters and their interactions served as the input vectors for the artificial neural network model, with the percentage urea adsorption onto fecal ash serving as the output. The Levenberg–Marquardt (TRAINLM) and Bayesian regularization (TRAINBR) techniques with mean square error (MSE) were trained and tested for predicting percentage adsorption. TRAINBR was demonstrated in our study to be an ideal match for improving urea adsorption, with an accuracy of R = 0.9982 and a convergence time of seven seconds. The ideal conditions for maximum urea adsorption were determined to be a high starting concentration of 13.5 g.L⁻¹; a low temperature of 30 °C, and a loading of 1.0 g of adsorbent. For urea, the improved settings resulted in maximum adsorption of 92.8%.