A multi-period multi-objective life cycle optimization model of a microalgal biorefinery integrating resource recirculation and quality considerations

• Main Author: Solis, Celine Marie A.
• Format: text
• Language: English
• Published: Animo Repository 2021
• Subjects: Algal biofuels > Refining; Life cycle costing; Biomass energy; Industrial Engineering
• Online Access: https://animorepository.dlsu.edu.ph/etdm_induseng/1; https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1000&context=etdm_induseng
• Institution: De La Salle University

To address the inevitable rise in global energy consumption, renewable energy sources have been continuously researched. The use of algal biomass as feedstock for biodiesel production has been recognized due to its higher lipid productivity compared to other feedstock and lower impact compared to fossil fuels. Yet, the production of algal biofuel faces challenges with regards to its commercialization since fossil fuels are generally more inexpensive. The integration of various processes yielding different bioproducts has been encouraged to increase profitability and overall sustainability for the algal biorefinery. Moreover, considering a zero-waste ideology, resource recirculation can be maximized to satisfy economic and environmental sustainability through the minimization of waste flows. Yet, existing modelling studies have not investigated on the resulting quality degradation linked to the continuous recovery and recirculation of resources within the biofuel production system. To address this, a multi-period multi-objective nonlinear optimization model for an algal biorefinery simultaneously optimizing cost and environmental impact, integrating life cycle assessment to properly account for process unit environmental impacts, and most importantly incorporates quality degradation resulting from resource recirculation for a closed-loop production system. Quality degradation and recovery functions are incorporated into the model, linked to material and product quality indicators through the model constraints. Quality equations used to determine the values for the product quality involve the quality from the previous process and the quality of the input material to be used in the given process. An algal biorefinery with biodiesel, glycerol, biochar, and fertilizer with a study period of 10 years was used as the base production system for the case study for model validation. Results show that process unit and input material selection is dependent on the purchase unit costs for each material. This is indicated by the fact that the model chooses only one input material for certain processes when given the chance to select two for slightly increased productivity. For the first half of the study period, the purchasing behavior showed a decreasing trend for all inputs because of the higher cost of purchasing new materials compared to holding costs. However, as more input materials are recovered over time, the resulting end product qualities decrease. This is observed during the latter portion of the study period, where purchase behavior began to increase, thereby lessening inventory, attributable to the minimum product quality constraint of 99.6% that had been set as a standard. It is also observed that as the input material quality decreases over time, the resulting recovery amounts for reuse also decreases. This is due to the fact that the model will choose to purchase more new resources to increase overall average quality for input materials. Scenarios have been applied to the base optimization model to investigate the corresponding effects. Experiments found that a minimum of 6 years of operation is essential for the algal biorefinery to make up for the investment and operating costs incurred. Test results also show that increasing purchase costs yield an overall higher purchasing behavior for upstream process input materials due to the higher purchase costs for downstream process inputs. Demand fluctuations applied to the model show that the ending inventory of input resources are positively correlated to the annual demand. Lastly, experimental incorporation of quality degradation and recovery functions show proof that as quality degrades at a faster rate, input purchasing behavior increases to make up for the resulting quality loss.