Simulation Modeling for Reliable Biomass Supply Chain Design Under Operational Disruptions

Abstract

Lignocellulosic biomass derived fuels and chemicals are a promising and sustainable supplement for petroleum-based products. Currently, the lignocellulosic biofuel industry relies on a conventional system where feedstock is harvested, baled, stored locally, and then delivered in a low-density format to the biorefinery. However, the conventional supply chain system causes operational disruptions at the biorefinery mainly due to seasonal availability, handling problems, and quality variability in biomass feedstock. Operational disruptions decrease facility uptime, production efficiencies, and increase maintenance costs. For a low-value high-volume product where margins are very tight, system disruptions are especially problematic. In this work we evaluate an advanced system strategy in which a network of biomass processing centers (depots) are utilized for storing and preprocessing biomass into stable, dense, and uniform material to reduce feedstock supply disruptions, and facility downtime in order to boost economic returns to the bioenergy industry. A database centric discrete event supply chain simulation model was developed, and the impact of operational disruptions on supply chain cost, inventory and production levels, farm metrics and facility metrics were evaluated. Three scenarios were evaluated for a 7-year time-period: (1) bale-delivery scenario with biorefinery uptime varying from 20 to 85%; (2) pellet-delivery scenario with depot uptime varying from 20 to 85% and biorefinery uptime at 85%; and (3) pellet-delivery scenario with depot and biorefinery uptime at 85%. In scenarios 1 and 2, tonnage discarded at the field edge could be reduced by increasing uptime at facility, contracting fewer farms at the beginning and subsequently increasing contracts as facility uptime increases, or determining alternative corn stover markets. Harvest cost was the biggest contributor to the average delivered costs and inventory levels were dependent on facility uptimes. We found a cascading effect of failure propagating through the system from depot to biorefinery. Therefore, mitigating risk at a facility level is not enough and conducting a system-level reliability simulation incorporating failure dependencies among subsystems is critical.

Modeling Biowaste Biorefineries: A Review

“…The modeling of biorefinery supply chain networks is steadily gaining interest (Martinkus et al, 2018;Sharma et al, 2018). Due to general concerns regarding the overall sustainability of using single food crops as biorefinery feedstocks, research focusses have gradually shifted toward the use of biowaste streams as biorefinery feedstocks (see section 2.2).…”

Development of an ammonia pretreatment that creates synergies between biorefineries and advanced biomass logistics models

“…As such, biomass storage and long-haul shipment to mega-biorefineries can be simplified. 32,33 We note also that paying farmers more for biomass induces them to grow much more biomass, leading to shorter supply chains with reduced transportation costs and much larger biorefineries ( potentially with capacities greater than 20 000 Mg per day), with the attendant economies of scale and only small effects on biofuel selling price. 34 Overall, the issue of biorefinery scale and the associated logistics needs much more study.…”

Logistics and Supply Chain Modelling for the Biobased Economy: A Systematic Literature Review and Research Agenda

“…The biobased economy value chain is integrated with the food and feed production chain through biorefineries that use biomass to deliver biomass components that can be used to make biobased products. Biomass that is used as a feedstock is an organic substance of one of the following types: agricultural crops and residues including lignocellulosic crops and residues (wood, grasses, or non-edible parts of plants) and food crops, fresh biomass (such as grass) and aquatic biomass, such as algae and seaweed (Sharma et al, 2018).…”