Algal biofuel supply chain network design with variable demand under alternative fuel price uncertainty: A case study

Arabi, Mahsa; Yaghoubi, Saeed; Tajik, Javad

doi:10.1016/j.compchemeng.2019.106528

Cited by 41 publications

(12 citation statements)

References 49 publications

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“…Even though it is distinguished that the production of biofuel from various agricultural and other wastes achieves higher overall performance compared to other technologies (Khishtandar et al, 2017), microalgal high sugar and oil is a promising source for biofuel production. For such production chains, FTOPSIS is the most valuable method to deal with the uncertainties of inventory deterioration issue, the applicability and efficiency of microalgal harvesting and the ranking of cultivation sites (Arabi et al, 2019;Peng et al, 2020).…”

Section: Microalgal Selection Problem and Ftopsis-related Workmentioning

confidence: 99%

Optimizing Microalgal Biomass Feedstock Selection for Nanocatalytic Conversion Into Biofuel Clean Energy, Using Fuzzy Multi-Criteria Decision Making Processes

2021

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Biofuel production from microalgae non-food feedstock is a challenge for strengthening Green energy nowadays. Reviewing the current technology, there is still reluctance in investing towards the production of new algal strains that yield more oil and maximize capital gains. In the current work, the microalgal feedstock selection problem is investigated for increased lipid production and nano-catalytic conversion into clean biofuel. For that purpose, a variety of Fuzzy Multi-Criteria Decision Making processes and a multitude of Optimization criteria spanning to technological, environmental, economic, and social aspects are used. The strains selected for the analysis are Chlorella sp., Schizochytrium sp., Spirulina sp., and Nannochloropsis sp. The methods applied are fuzzy analytic hierarchy process, FTOPSIS (fuzzy technique for the order of preference to the ideal solution), and FCM (fuzzy cognitive mapping). Pairwise comparison matrices were calculated using data from extensive literature review. All aforementioned fuzzy logic methodologies are proven superior to their numeric equivalent under uncertain factors that affect the decision making, such as cost, policy implications, and also geographical and seasonal variation. A major finding is that the most dominant factor in the strain selection is the high lipid content. Moreover, the results indicate that the Chlorella Vulgaris microalgae is ranked as the best choice by the FTOPSIS method followed by the Nannochloropsis strain, and Spirulina Platensis was found to be the last in performance. The best and worst case scenario run with FCM experimentally verify this choice indicating that Chlorella Vulgaris follows this trend of selection mostly with the technological and the economic criteria for both the sigmoid and the hyperbolic tangent deep-learning functions used.

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Section: Microalgal Selection Problem and Ftopsis-related Workmentioning

confidence: 99%

Optimizing Microalgal Biomass Feedstock Selection for Nanocatalytic Conversion Into Biofuel Clean Energy, Using Fuzzy Multi-Criteria Decision Making Processes

2021

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“…The objective is to minimize the overall cost, TC, of biodiesel supply chain network, presented in Equation (22).…”

Section: Objective Functionmentioning

confidence: 99%

“…Arabi, Yaghoubi, and Tajik [22] developed a multi-objective mixed-integer quadratic programming (MMIQP) model and a two-stage stochastic programming model for studying the design of algae-based biofuel supply chains. The objectives were to maximize the total profit and the total CO 2 absorption of the supply chain.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Algal Biomass to Biodiesel Supply Chain: Case Studies of the State of Oklahoma and the United States

et al. 2020

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The goal of this work is to design a supply chain network that distributes algae biomass from supply locations to meet biodiesel demand at specified demand locations, given a specified algae species, cultivation (i.e., supply) locations, demand locations, and demand requirements. The final supply chain topology includes the optimum sites to grow biomass, to extract algal oil from the biomass, and to convert the algae oil into biodiesel. The objective is to minimize the overall cost of the supply chain, which includes production, operation, and transportation costs over a planning horizon of ten years. Algae production was modeled both within the U.S. State of Oklahoma, as well as the entire contiguous United States. The biodiesel production cost was estimated at $7.07 per U.S. gallon ($1.87 per liter) for the State of Oklahoma case. For the contiguous United States case, a lower bound on costs of $13.68 per U.S. gallon ($3.62 per liter) and an upper bound of $61.69 ($16.32 per liter) were calculated, depending on the transportation distance of algal biomass from production locations.

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“…19 For example, Arabi et al 20 proposed a multiobjective microalgae-based biobutanol supply chain model in Iran and used it to analyze trade-offs between cost optimization and environmental sustainability in the supply chain. Arabi et al 21 also developed a microalgae-based biobutanol supply chain model with a single objective function of maximizing profit. Similarly, a multistage stochastic model of a microalgae-based biofuel supply chain has been proposed.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of the Biofuel Supply Chain Utilizing Multiple Feedstocks: A Korean Case Study

Zarei

Niaz

Dickson

et al. 2021

ACS Sustainable Chem. Eng.

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As the global climate crisis worsens and fossil fuels deplete, renewable energy sources have become more appealing. Biofuel is one of the promising sources produced from multiple biomass sources, as long as it does not compromise food security. In this direction, a multiperiod mixed-integer linear programming model incorporating three types of biomass simultaneously is developed to provide a second- and third-generation biofuel supply chain. The proposed model minimizes the total annualized cost by selecting raw materials, locating production facilities, placing storage facilities, and identifying optimal material flows by defining a deference path for each feedstock. A case study addressing bioethanol production in South Korea was then conducted to evaluate the performance of the developed model; the results demonstrated a clear reduction in the total annualized cost by incorporating multiple feedstocks. Using multiple feedstocks allowed for the total ethanol demand of South Korea to be met using harvested biomass, thereby drastically reducing import costs. Refinery costs accounted for the large majority of the remaining costs. A sensitivity analysis was also conducted to assess the effect of uncertainty associated with some economic and production parameters.

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Algal biofuel supply chain network design with variable demand under alternative fuel price uncertainty: A case study

Cited by 41 publications

References 49 publications

Optimizing Microalgal Biomass Feedstock Selection for Nanocatalytic Conversion Into Biofuel Clean Energy, Using Fuzzy Multi-Criteria Decision Making Processes

Optimizing Microalgal Biomass Feedstock Selection for Nanocatalytic Conversion Into Biofuel Clean Energy, Using Fuzzy Multi-Criteria Decision Making Processes

Optimization of the Algal Biomass to Biodiesel Supply Chain: Case Studies of the State of Oklahoma and the United States

Optimal Design of the Biofuel Supply Chain Utilizing Multiple Feedstocks: A Korean Case Study

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