Due to the rapid increase in environmental degradation and depletion of natural resources, the focus of researchers is shifted from economic to socio-environmental problems. Blockchain is a disruptive technology that has the potential to restructure the entire supply chain for sustainable practices. Blockchain is a distributed ledger that provides a digital database for recording all the transactions of the supply chain. The main purpose of this research is to explore the literature relevant to blockchain for sustainable supply chain management. The focus of this review is on the sustainability of the blockchain-based supply chain concerning environmental conservation, social equality, and governance effectiveness. Using a systematic literature review, a total of 136 articles were evaluated and categorized according to the triple bottom-line aspects of sustainability. Challenges and barriers during blockchain adoption in different industrial sectors such as aviation, shipping, agriculture and food, manufacturing, automotive, pharmaceutical, and textile industries were critically examined. This study has not only explored the economic, environmental, and social impacts of blockchain but also highlighted the emerging trends in a circular supply chain with current developments of advanced technologies along with their critical success factors. Furthermore, research areas and gaps in the existing research are discussed, and future research directions are suggested. The findings of this study show that blockchain has the potential to revolutionize the entire supply chain from a sustainability perspective. Blockchain will not only improve the economic sustainability of the supply chain through effective traceability, enhanced visibility through information sharing, transparency in processes, and decentralization of the entire structure but also will help in achieving environmental and social sustainability through resource efficiency, accountability, smart contracts, trust development, and fraud prevention. The study will be helpful for managers and practitioners to understand the procedure of blockchain adoption and to increase the probability of its successful implementation to develop a sustainable supply chain network.

This study investigated the co-combustion of the blends of coal and biomass residues from poplar sawdust, rice husk, pine nut shells, and sunflower residues for ecofriendly energy production. Proximate and ultimate analyses and calorific values of the coal and biomass residues were also carried out to evaluate the properties of the coal and biomass residues. The volatile matter in coal was reported as 43.38 wt% and ranged from 56.76 wt% to 80.95 wt% in the biomass residues. The ultimate analysis reported the carbon and sulfur content of coal as 68.7 wt% and 5.5 wt%, respectively. The coal and biomass blends were prepared using different ratios on the thermal basis of coal and biomass given as 100:0, 90:10, 80:20, 70:30, 60:40, and 50:50 by weight percentage. The consequent stoichiometric air requirements for all the blends were also calculated. The results revealed that the combustion of 60:40 of coal and sunflower residue blend was the most efficient blend, resulting in less emission of NO_x, SO_x, and CO₂ in the flue gas compared to the combustion of pure coal. The study revealed a great perspective of the selected biomass residues to blend with coal for environmentally friendly and sustainable energy production.

Biomass gasification is the most reliable thermochemical conversion technology for the conversion of biomass into gaseous fuels such as H₂, CO, and CH₄. The performance of a gasification process can be estimated using thermodynamic equilibrium models. This type of model generally assumes the system reaches equilibrium, while in reality the system may only approach equilibrium leading to some errors between experimental and model results. In this study non-stoichiometric equilibrium models are modified and improved with correction factors inserted into the design equations so that when the Gibbs free energy is minimized model predictions will more closely match experimental values. The equilibrium models are implemented in MatLab and optimized based on experimental values from the literature using the optimization toolbox. The modified non-stoichiometric models are shown to be more accurate than unmodified models based on the calculated root mean square error values. These models can be applied for various types of solid biomass for the production of syngas through biomass gasification processes such as wood, agricultural, and crop residues.

To help meet the global demand for energy and reduce the use of fossil fuels, alternatives such as the production of syngas from renewable biomass can be considered. This conversion of biomass to syngas is possible through a thermochemical gasification process. To design such gasification systems, model equations can be formulated and solved to predict the quantity and quality of the syngas produced with different operating conditions (temperature, the flow rate of an oxidizing agent, etc.) and with different types of biomass (wood, grass, seeds, food waste, etc.). For the comparison of multiple different types of biomass and optimization to find optimal conditions, simpler models are preferred which can be solved very quickly using modern desktop computers. In this study, a number of different stoichiometric thermodynamic models are compared to determine which are the most appropriate. To correct some of the errors associated with thermodynamic models, correction factors are utilized to modify the equilibrium constants of the methanation and water gas shift reactions, which allows them to better predict the real output composition of the gasification reactors. A number of different models can be obtained using different correction factors, model parameters, and assumptions, and these models are compared and validated against experimental data and modelling studies from the literature.