Sugarcane leaves are the main residue constitute approximately 14% of the total weight of the remaining sugarcane after harvesting. An effective method for processing sugarcane leaves residues is needed at low cost without causing any environmental problem. This research aims to disclose the effect of sugarcane leaves densification method applied prior to pyrolysis process in a pilot scale reactor. To evaluate the process and its product, the experiments were carried out into two types: (i) pyrolysis of sugarcane leaves without densification at 320 °C with a variation of pyrolysis time for 100, 120, and 130 minutes and (ii) pyrolysis of densified sugarcane leaves with the variation of pyrolysis temperature 320 °C and 420 °C. The investigated conditions showed that the effect of sugarcane leaves densification prolong the pyrolysis time up to 240 minutes at a pyrolysis temperature of 320 °C, and increased the yield of biochar and bio-oil products up to 41% and 38%, respectively. However, in term of the physical properties of biochar products, the fixed carbon content decreased by 7% when the sugarcane leaves were compacted. While other parameters found no significant difference in pyrolysis at 320 °C, the effect of sugarcane leaves densification is very beneficial especially when the pyrolysis was performed at 420 °C.

We developed a Fe₃O₄/SiO₂/CaCO₃ magnetic nanocomposite adsorbent, with SiO₂ synthesized from sea sand and CaCO₃ derived from coral skeletons. The Fe₃O₄/SiO₂/CaCO₃ nanocomposite was characterized and employed as an adsorbent to reduce ammonia levels in shrimp pond wastewater where ammonia concentrations ranged from 11.9 to 38.8 mg/L. We further explored the effects of various parameters on the removal efficiency, adsorption capacity, thermodynamics, isoterm, and kinetics of the adsorption process. Specifically, we examined the influence of pH (3–8), adsorbent mass (0.025–0.25 g), temperature (27–60 °C), and contact time (10–120 min). Ammonia concentrations in the filtrate were measured using the Nessler method. The synthesis of CaCO₃ from coral skeleton, SiO₂ from sand, and Fe₃O₄/SiO₂/CaCO₃ adsorbent was successfully achieved, as confirmed by XRF, FTIR, and XRD characterizations. The adsorption process adhered to the second-order kinetics model, exhibited spontaneous behavior with a negative ΔG value, and followed the Langmuir isotherm model (R²=0.9267). The results indicated an optimal adsorbent mass of 0.025 g, achieving 89.3% adsorption at 60 minutes of contact time, a temperature of 27 °C, and an optimal pH of 5. When applied to shrimp pond wastewater, the Fe₃O₄/SiO₂/CaCO₃ adsorbent demonstrated an adsorption efficiency ranging from 52.1% to 86.8% and an adsorption capacity between 6.2 and 30.9 mg/g.

Proper water resource management is essential for maintaining a sustainable supply chain and meeting water demand. The urgent need to preserve river ecosystems by sustaining environmental flow (EF) in the realm of environmental management has been highlighted by the drastic changes to river ecosystems and upstream flow dynamics brought about by careless river exploitation in the last few decades. To optimize EF in river basin management, we present an integrated modeling approach. We focused on the Pir Khezran River basin. Our objective was to estimate EF and generalize the findings to adjacent rivers using modeling techniques, thus providing valuable insights for environmental management applications. The assessment and optimization of EF under uncertain conditions was achieved by combining physical habitat simulation (PHABSIM) modeling with advanced techniques like Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Multilayer Perceptron (MLP) neural networks. This integrated modeling approach contributes to sustainable solutions for river basin management and environmental conservation by effectively optimizing EF, as demonstrated by the results. This research, therefore, makes valuable contributions to environmental management in various areas such as ecological preservation, modeling and optimizing environmental systems, and policy considerations.

In this review, we describe recent developments and strategies involved in the utilization of solid supports for the management of wastewater by means of biological treatments. The origin of wastewater determines whether it is considered natural or industrial waste, and the source(s) singly or collectively contribute to increase water pollution. Pollution is a threat to aquatic and humans; thus, before the discharge of treated waters back into the environment, wastewater is put through a number of treatment processes to ensure its safety for human use. Biological treatment or bioremediation has become increasingly popular due to its positive impact on the ecosystem, high level of productivity, and process application cost-effectiveness. Bioremediation involving the use of microbial cell immobilization has demonstrated enhanced effectiveness compared to free cells. This constitutes a significant departure from traditional bioremediation practices (entrapment, adsorption, encapsulation), in addition to its ability to engage in covalent bonding and cross-linking. Thus, we took a comparative look at the existing and emerging immobilization methods and the related challenges, focusing on the future. Furthermore, our work stands out by highlighting emerging state-of-the-art tools that are bioinspired [enzymes, reactive permeable barriers linked to electrokinetic, magnetic cross-linked enzyme aggregates (CLEAs), bio-coated films, microbiocenosis], as well as the use of nanosized biochar and engineered cells or their bioproducts targeted at enhancing the removal efficiency of metals, carbonates, organic matter, and other toxicants and pollutants. The potential integration of ‘omics’ technologies for enhancing and revealing new insights into bioremediation via cell immobilization is also discussed.

Amidst the adverse effects of the existing linear model on economic, social, and environmental aspects, the circular economy has emerged as a more sustainable alternative for economic growth and environmental sustainability. Further, the increasing significance of the circular economy as a means to achieve sustainability has prompted researchers to suggest various approaches. In this study, we employed a systematic literature review to present a unified perspective on the concept of circular economy and emphasize the connection of it with eco-innovation. We found that the last three years have witnessed a sudden rise in the publications related to a circular economy, stating the rise in interest of researchers towards a circular economy. While in its nascent phase of execution, much research has been carried out on the importance of adopting a circular economy for achieving sustainability. However, most researchers have failed to explore the contribution of the circular economy in the UN sustainable development strategies (SDGs) and the role of eco-innovation in the circular economy. Therefore, this research is novel, and we explored the role of circular economy and eco-innovation towards sustainability through a systematic literature review. This research indicated that while developed countries have shifted to a circular economy, developing countries need to transition to a circular economy for sustainable benefits. A correlation was also found between the circular economy and some SDGs. The results emphasized that the incorporation of eco-innovations can help in mitigating the environmental consequences of production and consumption activities. It also sheds light on notable instances of eco-innovations that have been developed for deployment in the circular economy. This research will be helpful to industries, universities, and other major sections of society in developing circular economy practices in their core processes and ensuring the attainment of sustainable development goals.

Air and oil leaks are two of the predominant operational failures in metro trains, which can cause severe issues and a lot of downtime. Predictive maintenance on such machinery can be of great use. This work aimed to develop a deep learning algorithm for fault analysis in metro trains. The MetroPT dataset was used for this work. A multi-task artificial neural network was developed for the simultaneous identification of failures and GPS quality assessment. The network had common dense, batch normalization, and Gaussian noise layers, followed by output sigmoid layers for each output. The algorithm was trained for 20 epochs with a batch size of 5000 using the using Adam optimizer. The local interpretable model agnostic explanations (LIME) technique was used to provide explanations for the model predictions. Finally, a dashboard was developed for the same application consisting of the best-trained algorithm for decision-making, along with trend visualizations and explanations. The developed multi-task model produced 98.89%, 99.12%, and 99.24% accuracies in the testing set for failure type, failure location, and GPS quality predictions, respectively. The model produced 99.56%, 99.67%, and 99.84% precision in the testing set for failure type, failure location, and GPS quality predictions, respectively. The loss values for the trained model on the testing set were 0.0035, 0.0026, and 0.0033 for the three tasks, respectively. The deep learning model took 43 seconds for training and 1 second for inferencing for test data. The LIME technique produced explanations for each predictive task with feature importance in positive and negative impacts. On the whole, the proposed framework can be effective for fast and accurate fault analysis in metro trains.