Organic-waste-derived butyric acid-to-biodiesel supply-chain network: Strategic planning design using a deterministic snapshot model

Biodiesel supply chain network design: a comprehensive review with qualitative and quantitative insights

The global community is actively pursuing alternative energy sources to mitigate environmental concerns and decrease dependence on fossil fuels. Biodiesel, recognized as a clean and eco-friendly fuel with advantages over petroleum-based alternatives, has been identified as a viable substitute. However, its commercialization encounters challenges due to costly production processes. Establishing a more efficient supply chain for mass production and distribution could surmount these obstacles, rendering biodiesel a cost-effective solution. Despite numerous review articles across various renewable energy supply chain domains, there remains a gap in the literature specifically addressing the biodiesel supply chain network design. This research entails a comprehensive systematic literature review (SLR) focusing on the design of biodiesel supply chain networks. The primary objective is to formulate an economically, environmentally, and socially optimized supply chain framework. The review also seeks to offer a holistic overview of pertinent technical terms and key activities involved in these supply chains. Through this SLR, a thorough examination and synthesis of existing literature will yield valuable insights into the design and optimization of biodiesel supply chains. Additionally, it will identify critical research gaps in the field, proposing the exploration of fourth-generation feedstocks, integration of multi-channel chains, and the incorporation of sustainability and resilience aspects into the supply chain network design. These proposed areas aim to address existing knowledge gaps and enhance the overall effectiveness of biodiesel supply chain networks.

Robust sustainable canola oil-based biodiesel supply chain network design under supply and demand uncertainty

The excessive consumption of fossil fuels has sparked debates and caused environmental damage, leading the global community to search for a suitable alternative. To achieve sustainable development goals and prevent harmful climate scenarios, the world needs to increase its use of renewable energy. Biodiesel, a clean and eco-friendly fuel with a high flash point and more lubrication than petroleum-based fuels, and without the emission of harmful environmental gases, has emerged as one of the fossil fuel alternatives. To promote the mass-level production of biodiesel, a sustainable supply chain (SC) that does not depend on laboratory production is necessary. For this purpose, this research proposes a multi-objective mixed-integer non-linear mathematical programming (MINLP) model to design a sustainable canola oil-based biodiesel supply chain network (CO-BSCND) under supply and demand uncertainty. This mathematical model aims to minimize the total cost (TC) and total carbon emission while maximizing the total number of job opportunities simultaneously. A scenario-based robust optimization (SBRO) approach is applied to deal with uncertainty. The proposed model is implemented in a real case study in Iran, and numerical experiments and sensitivity analysis are conducted to demonstrate its applicability. The results of this research demonstrate that designing a sustainable supply chain network for the production and distribution of biodiesel fuel is achievable. Moreover, this mathematical modeling makes mass-scale production of biodiesel fuel a possibility. In addition, the SBRO method adopted in this research enables managers and researchers to explore the design conditions of the supply chain network by controlling the uncertainties that affect it. This approach allows the chain's performance to be as close as possible to the actual conditions. As a result, the SBRO method enhances the efficiency of the supply chain network and boosts productivity toward achieving desired goals.

Organic waste derived bioethanol supply chain network: Multiobjective snapshot model with a real-Korea case study

Bioethanol, a promising biofuel gasoline additive, was recently produced by a new technology using acetic acid derived from organic waste. This study develops a multiobjective mathematical model with two competing minimization objectives: economy and environmental impact. The formulation is based on a mixed integer linear programming approach. The configuration of the organic-waste (OW)-based bioethanol supply chain network is optimized in terms of the number and locations of bioethanol refineries. The flows of acetic acid and bioethanol between the geographical nodes must meet the bioethanol regional demand. The model is validated in three real-scenario case studies with different OW utilization rates (30%, 50%, and 70%) in South Korea in the near future (2030). The multiobjective problem is solved using the ε-constraint method and the selected Pareto solutions balance the trade-off between the economic and environmental objectives. At the "best-choice" solution points, increasing the OW utilization rate from 30% to 70% decreased the total annual cost from 904.2 to 707.3 million $/yr and the total greenhouse emissions from 1087.2 to -15.7 CO₂ equiv./yr.

Process evaluation and techno-economic analysis of biodiesel production from marine macroalgae Codium tomentosum

In the current study, a seaweed Codium tomentosum was used as a source for the production of biodiesel. The maximum oil from marine macroalgae was recovered using ultrasound-assisted pretreatment. The oil yield was found to be maximum at optimal conditions such as 5% biomass wetness, 0.18 mm biomass size, 6:1 extraction solvent: biomass ratio, extraction temperature, and time as 55 °C and 140 min respectively. The extracted oil was transesterified using solidsolid nanocatalyst produced from waste clay doped with Zn. The maximum biodiesel conversion was found to be 90.5% at optimum conditions. The marine macroalgae C. tomentosum was found to be one of the potential sources for biodiesel production. The techno-economic analysis of the overall biodiesel production (20 MT/batch) process was investigated. The plant payback period is 8.59 years with a positive NPV of 1381 M$/yr.

Green-resilient supply chain network design for perishable products considering route risk and horizontal collaboration under robust interval-valued type-2 fuzzy uncertainty: A case study in food industry

A green and resilient (G-Resilient) supply chain network is designed for perishable products under disruption risks and epistemic uncertainties. This study aims to minimize effects of the disruption by presenting new strategies, such as multiple sourcing, financial suppliers, horizontal collaboration, route risk, and coverage radius, in designing a new multi-objective mixed-integer linear programming model for multi-product, multi-period, multi-modal G-Resilient supply chain. Then, a novel robust possibilistic programming (RPP) approach is presented using credibility measure and membership functions of generalized interval-valued type-2 fuzzy variables to face the epistemic uncertainties, such as supply capacity of facilities, customer demand, transportation cost, and CO₂ emission factor, in the proposed mathematical model. An improved version of augmented ε-constraint method (AUGMECON2) is also employed to produce separate Pareto-optimal solutions. Moreover, the study compares the proposed RPP with possibilistic chance-constrained programming model and illustrates its advantages; in the standard deviation of CO₂ emission objective function, its performance has improved by about 44.91%. Finally, the model's performance has been verified by a real case study in the food industry, and managerial implications have been provided.

Sustainable planning and decision-making model for sugarcane mills considering environmental issues

Many companies and organizations are pursuing "carbon footprint" projects to estimate their own contribution due to growing concerns about global climate change and carbon emissions. Measures such as carbon taxes are the most powerful means of dealing with the threats of climate change. In recent years, researchers have shown a particular interest in modelling supply chain networks under this scheme. Disorganized disposal of by-products from sugarcane mills is the inspiration of this research. In order to connect the problem with the real world, the proposed sustainable sugarcane supply chain network considers carbon taxes on the emission from industries and during transportation of goods. The presented mixed-integer linear programming modelling is a location-allocation problem and, due to the inherent complexity, it is considered a Non-Polynomial hard (NP-hard) problem. To deal with the model, three superior metaheuristics Genetic Algorithm (GA), Simulated Annealing (SA), Social Engineering Optimizer (SEO) and hybrid methods based on these metaheuristics, namely, Genetic-Simulated Annealing (GASA) and Genetic-Social Engineering Optimizer (GASEO), are employed. The control parameters of the algorithms are tuned using the Taguchi approach. Subsequently, one-way ANOVA is used to elucidate the performance of the proposed algorithms, which compliments the performance of the proposed GASEO.

Recent advances in valorization of organic municipal waste into energy using biorefinery approach, environment and economic analysis

Researcher's all around works on a copious technique to lessen waste production and superintend the waste management for long-term socio-economic and environmental benefits. Value-added products can be produced from municipal waste by using holistic and integrated approaches. In this review, a detail about the superiority of the different methods like anaerobic digestion, biofuel production, incineration, pyrolysis and gasification were used for the conversion of municipal waste to feedstock for alternate energy and its economic- environmental impacts were consolidated. Most conversion techniques were environmentally friendly to manage municipal waste. The biological process was more economically feasible compare to the thermal process, for the reason thermal process required a large amount of capital investment and energy utilization. In the thermal process, gasification shows low emission, and pyrolysis shows low capital investment and economically feasible compare to other thermal processes. Waste to energy technology significantly reduced the emission and energy demand.