(677b) Cellulosic Ethanol Production: Improving Viability through Enterprise-Wide Modeling and Optimization

In recent years there has been a marked surge in the search for alternative sources of energy that wean the world off of dependence on fossil fuels and reduce our carbon foot-print on this world. Of the many sources of energy investigated, ethanol has been found to be one of the most promising resources to replace fossil fuels. After a boom in corn-based ethanol in the early part of the 21st century, the interest has gradually shifted towards more viable sources of ethanol. Cellulosic Ethanol is one such form that is extremely attractive owing to the fact that the raw materials can be composed completely of ?left-over? wastes of food crops and forest harvests.

Even with increased research emphasis on cellulosic ethanol production technologies, large scale production is still hampered by many factors. Some of these factors that have received considerable interest in recent times include genetically engineered enzymes and microbes that are more efficient and resistant to poisoning by by-products and improved process technologies that can render large scale production of cellulosic ethanol a more profitable option for private entities. One area that has not received enough research interest in recent times is enterprise wide modeling and optimization in order to make the entire enterprise more competitive in the energy sector.

Modeling an enterprise that produces cellulose based ethanol can provide valuable insight into the inter-play of the ethanol supply-chain. This idea had been motivated by a recent surge in the research areas concerning supply-chain optimization and how cost-cutting measures throughout a supply-chain can render an otherwise sluggish enterprise, profitable. In this work we propose modeling the ethanol producing enterprise as an entity comprising of three functionally dependant levels?the corporate level, the supply-chain level and the production level.

The model for the corporate level represents the entire hierarchy of the enterprise with an optimization model. The objective function used in the model is the company's shareholder value and the optimization time horizon is five years with one-month time increments. A stochastic demand forecasting model provides the model with monthly demand forecasts for the five years. The model includes decision variables such as asset acquisition, plant capacity increments, types of feedstock to grow depending on the seasonal crop yields, how much land to use to grow feedstock, monthly production and inventory targets, what markets to serve, and how many distribution centers to maintain. The constraints and parameters introduced in the model include amongst others, government tax subsidies, federal grants, and, loan obligations. By doing a sensitivity analysis on such a model, one can determine key decision variables that can improve a company's shareholder value.

The supply chain level receives monthly inventory targets, production levels, and sales volumes as constraints from the corporate level, and optimizes the supply chain model with the objective of maximizing profit. The time horizon for the model is one month with one-week time increments. The constraints included in the model include capacity constraints, and raw material and final product balances. More rigorous modeling for departments within the supply chain, such as the inventory and transportation department, can also be done to optimize their functioning. A stochastic demand forecasting model provides the model with weekly demand forecasts for the given time horizon. The decision variables of the model include inventory profiles, production profiles, and sales profiles.

The production level receives the weekly production targets from the supply chain level and optimizes the production facilities with the objective of minimizing operating costs. The time horizon for the model is one week with daily time increments. We propose a simulation of the process be included in this level to simulate the effects of the decisions of the other levels on the production facilities. Key decision variables yielded by the model are the operating points for the facilities. Daily tasks such as process monitoring, fault detection and diagnosis, and data reconciliation and plant-wide optimization can also be carried out.

Since information sharing is a key ingredient to achieve enterprise wide integration, a multi-agent system is envisioned to provide the system with the necessary framework to integrate the three levels of the enterprise.

With the aforementioned hierarchy a budding cellulosic ethanol production enterprise can achieve distinct cost-cutting in its operations. The models provided are rigorous enough to lend insight into key decision variables on all three levels. Such an analysis can provide the enterprise with key areas to focus their resources on in order to sustain their operations.