Flexographic printing is a highly sought-after technique within the realm of packaging and labeling due to its versatility, cost-effectiveness, high speed, high-quality images, and environmentally friendly nature. A major challenge in flexographic printing is the need to optimize energy usage, which requires diligent attention to resolve. This research combines lean principles and machine learning to improve energy efficiency in selected flexographic printing machines; i.e., Miraflex and F&K. By implementing the 5Why root cause analysis and Kaizen, the study found that the idle time was reduced by 30% for the Miraflex machine and the F&K machine, resulting in energy savings of 34.198% and 38.635% per meter, respectively. Additionally, a multi-linear regression model was developed using machine learning and a range of input parameters, such as machine speed, production meter, substrate density, machine idle time, machine working time, and total machine run time, to predict energy consumption and optimize job scheduling. The results of the research exhibit that the model was efficient and accurate, leading to a reduction in energy consumption and costs while maintaining or even improving the quality of the printed output. This approach can also add to reducing the carbon footprint of the manufacturing process and help companies meet sustainability goals.

Increasing energy demand and the detrimental environmental impacts of fossil fuels have led to the development of renewable energy sources. Rapid demand growth for wood pellets over the last decade has established wood pellets as a potential renewable energy source in a globally competitive energy market. Integrated decision making including all stakeholders in the wood pellet supply chain (WPSC) is essential for a smooth transition to commercially viable wood pellet production. In this aspect, this study aims to suggest a decision support system for optimizing biomass-based wood pellet production supply chain network design (WPP-SCND). The WPP-SCND decision system minimizes the total supply chain (SC) cost of the system while also reducing carbon emissions associated with wood pellet SC activities. All objective parameters, including biomass availability at the supply terminals, market demand, and biomass production, are considered fuzzy to account for epistemic uncertainty. A fuzzy flexible robust possibilistic programming (fuzzy-FRPP) technique is developed for solving the suggested uncertain WPP-SCND model. The case findings show that the imprecise nature of the parameters has a significant impact on the strategic and tactical decisions in the wood pellet SC. By investing almost 10% of the total cost, robust decisions within the wood pellet SC can be obtained. It is established that the fuzzy-FRPP technique successfully provides robust decisions and achieves a balance between transportation costs, emissions costs, and economies of scale when making capacity decisions. Although the suggested decision support system is used to manage the production and distribution of wood pellets, the insights and solution methodology may be extended to the production of other biofuels. The proposed research may be valuable to authorities involved in planning large-scale wood pellet-related production-distribution projects.

This study aims to develop a multi-objective second-generation-based socially responsible supply chain (SGB-SRSC) network design model that considers all dimensions of sustainability: economy, environment, and social. The dynamic nature of the biodiesel supply chain (SC) impairs the SGB-SRSC model decisions; thus, a grey-fuzzy solution approach is developed. Biodiesel is a promising renewable energy resource produced from a variety of easily accessible domestic wastes. For a swift transition towards commercially feasible biodiesel production, integrated optimization of the biodiesel SC system is critical. Using the latest social impact assessment tools, this study provides a decision-support system for developing a biodiesel SC network. A comprehensive computational analysis is performed on a case study to validate the proposed model. The results show that significant investment is required to achieve social well-being goals and secure decisions against uncertainty associated with SGB-SRSC model parameters. Further, it is observed that the expenses of biodiesel production and biodiesel plant installation accounted for a large portion of the overall SC cost. As a result, finding more cost-effective biodiesel production methods is critical to the industry’s economic viability. Regulators and policymakers involved in biodiesel production projects may find the framework useful in obtaining a compromise solution for socio-economic goals based on their preferences.

The petroleum industry plays a vital role in the economies of developing countries. Refinery wastewater pollution has increased in recent years due to the increase in the industrial and urban use of petroleum products. The present work demonstrates how textile waste can be modified into a material that can effectively remove sulfur pollutants from refinery wastewater. An economic activated carbon (AC) material was developed by using cotton spinning waste from a textile company. The chemical activation was carried out using nitric acid (HNO₃), hydrogen peroxide (H₂O₂), and potassium hydroxide (KOH). The characterization of the obtained activated carbons was performed using the SEM, BET, and FTIR techniques. The effect of the experimental adsorption condition was investigated using both bed and batch isotherms. The results show that effluent flow has the highest effect on sulfur compound adsorption. The greatest adsorption capacity of the sulfur compounds was found to be around 168.4 mg·g⁻¹. The equilibrium data were investigated using the Freundlich, Langmuir, Dubinin–Radushkevich, Tóth, and Sips isotherm models. The Langmuir model exhibited the best fit (R² = 0.98) for the sulfur compounds’ adsorption, which implies that their adsorption onto the synthesized AC was homogeneous. The kinetic data were tested with pseudo-first-order, pseudo-second-order, and intraparticular diffusion equations. The pseudo-second-order equation described the kinetic data well (R² = 0.99), indicating that this adsorption may be restricted by the chemisorption process. These properties under optimal conditions make the obtained ACs suitable for use in refinery wastewater treatment.

The goal of this research is to develop a novel second-generation-based biogas supply chain network design (BG-SCND) model that takes into account the triple bottom line approach. Biogas is a promising renewable energy source that can be obtained from a variety of easily accessible second-generation wastes, including animal manure, municipal waste, and agricultural leftovers. Integrated optimization of the biogas generation system is essential for a speedy and environmentally friendly transition to sustainable biodiesel production. The dynamic environment of the energy market significantly impairs the decisions of the BG-SCND model; therefore, a hybrid solution approach using flexible programming and possibilistic programming is suggested. To verify the suggested model and approach for solving the problem, a thorough computational analysis of a case study is conducted. The case study findings demonstrate that considerable investment is necessary to attain social and environmental well-being goals and safeguard decisions against epistemic uncertainty. Policymakers involved in the planning of biogas production and distribution projects may find the proposed approach useful.

This investigation delves into the effectiveness of employing vegetable-based cutting fluids and nanoparticles in milling AZ31 magnesium alloy, as part of the pursuit of ecologically sustainable manufacturing practices. The study scrutinizes three different cutting environments: (i) dry cutting; (ii) minimum quantity lubrication (MQL) with rice bran oil as the base oil and turmeric oil as an additive; and (iii) MQL with rice bran oil as the base oil, and turmeric oil and kaolinite nanoparticles as additives. Fuzzy logic was implemented to develop the design of experiments and assess the impact of these cutting environments on carbon emissions, surface quality, and microhardness. Upon conducting an analysis of variance (ANOVA), it was determined that all the three input parameters (cutting environment, cutting speed, and feed) greatly affect carbon emissions. The third cutting environment (MQL + bio-oils + kaolinite) generated the lowest carbon emissions (average of 9.21 ppm) and surface roughness value (0.3 um). Confirmatory tests validated that the output parameters predicted using the multiobjective genetic algorithm aligned well with experimental values, thus affirming the algorithm’s robustness.