(93d) Optimal Technology Assessment and Product Portfolio Design for Sustainable Biorefineries

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. After a boom in U.S. corn-based ethanol in the early part of the 21st century, the interest has gradually shifted towards more viable sources of biofuels and biochemicals. Cellulosic ethanol, biodiesel, and syngas are examples of such fuels that are extremely attractive owing to the fact that the raw materials can be composed completely of “left-over” wastes of food crops and forest harvests that don't interfere with the human food chain and the natural ecosystem.

Even with increased research emphasis on biofuel and biochemical production technologies, large scale production is still hampered by many factors. Some of these factors that have received considerable interest in recent times include, cultivating more robust feedstock, genetically engineering enzymes and microbes that are more efficient and resistant to poisoning by by-products and improving process technologies that can render large scale production of biofuels a more profitable option for private entities. One area that has not received enough research interest in recent times is strategic modeling and optimization of biorefining enterprises in order to improve strategic decision-making. Such modeling can aid biorefineries to make more informed strategic decisions that will undoubtedly affect the long term profitability, growth, and sustainability of these ventures.

Strategic decisions for nascent biorefineries include product portfolio selection, technology and feedstock selection, and supply chain design and market selection.  A sustainable enterprise is often defined as an enterprise that does not have a negative socio-environmental impact on the society. We further refine this definition to encompass not only the ability to positively impact the environment, but also maintaining such an impact through value creation and profitability. An enterprise is defined as being sustainable if it produces goods and services that benefit our environment and is able to preserve such an influence through continued growth. Modeling the strategic decision process of an enterprise that produces renewable-based fuels and chemicals, using engineering and financial tools, can provide valuable insight into the inter-play of technology and process interactions for different product streams, hence providing valuable insights into the correlation between different processing routes which can in turn lead to substantial cost savings for the enterprise. This idea had been motivated by a recent surge in the research area concerning strategic optimization of engineering enterprises and how cost-efficient design of production facilities and supply chains can render an otherwise sluggish enterprise, profitable.

Our work tries to develop a systematic framework, within which strategic decisions a biofuels enterprise makes, can be evaluated. The framework developed is an optimization-based framework which integrates capital budgeting and operational decisions. The decision making process models the actual corporate structure of a process enterprise and is broken down into three concrete steps; the first step is a flexibility evaluation model which provides a preliminary set of portfolio choices of raw materials, suppliers, products, and technologies to the enterprise for detailed economic analysis. The second step evaluates the effects of uncertainty on portfolio choices and provides an incremental strategic plan regarding facility, capacity and portfolio design in order to minimize the downside risks of uncertainty while maintaining their maximum upside. The final step decides on a supply chain and network design, given a process and capacity plan.

The first step in the framework is a deterministic optimization model with the aim of selecting a preliminary feedstock, technology and product portfolio for detailed analysis and evaluating the effect of changing input parameters on the decision variables and the objective function (sensitivity analysis). The model is formulated as an MILP whose outputs include process and capacity design alternatives for different product portfolios. The objective function used in the model is the stakeholder value which is calculated using the free cash flow to firm method. The model formulation includes emissions analysis of process schemes and mandates mitigation of wastes. The constraints and parameters introduced in the model include amongst others, mass and cash balances, production and distribution capacity constraints, and demand constraints. The model decision variables include preliminary process integration schemes and plant capacity design and increments, feedstock selection with seasonal considerations, and semi-annual production, inventory, and sales profiles for each process configuration. Sensitivity analysis is carried out to determine what process configurations offer a judicious mix of design flexibility and profitability. The model acts as a screening model whose results are fed into the second step of the framework for detailed analysis.

The second step of the framework is a real-option based analysis tool formulated as a stochastic MILP problem. Real options analysis is a new paradigm in engineering that borrows techniques from the more established financial options analysis to evaluated assets typically possessed by any manufacturing enterprise. Such analysis has been shown to have practical importance for cases where model parameters have a high degree of uncertainty. A biorefinery of the future will be plagued by uncertainty in raw material prices and availability, product demands and prices, and technological evolution. In such cases, the option to drop (or carry out) a certain action at any point in time has intrinsic value (called real-options value or ROV) and the proposed module seeks to maximize the value of this option. A binomial tree is used to generate scenarios for uncertain parameters and a dynamic programming approach is used to evaluate the portfolio decisions at each time step. Additional constraints include debt balances and obligations, financial ratios and key performance indicators, and raw material availability constraints. The candidate portfolio solutions yielded by the screening model are fed into this module and timing decisions for entry into new feedstock, product and technology markets, raw material supplier selection, and capacity planning are evaluated using this tool.

Following the creation of a plan which includes the optimal process configuration and the corresponding capacity design, the third and final step in the framework is designing the supply chain network for movement of raw materials and finished goods. The structural optimization model mandates the production amounts prescribed by the real-options module leading to fixed revenue streams for the structural analysis module. Consequently, the objective function of the structural analysis module is minimization of total system costs over the optimization time horizon. The model constraints include budgetary constraints, transportation mode-related constraints, and environmental constraints. The outputs for the model include a supply chain configuration, supplier-facility and facility-market matching, transportation mode and network design, and monetary requirements for designing the network.   

The potential process scheme that is evaluated using the aforementioned architecture involves simultaneous production of biodiesel and ethanol (thermochemical or biochemical) with a centralized utility facility. Succinic acid is produced as a co-product of cellulosic ethanol with utilization of fermentation-related carbon dioxide emissions. Furthermore glycerol, a by-product of biodiesel, can be utilized in two ways; 1) fermented to produce ethanol, and/or 2) fermented to produce 1,3-propanediol.   

The outcomes of our approach for the above configuration are discussed in detail in the paper. The inclusion of environmental and social implications of enterprise activities using life cycle analyses leads us to define our objective function as the stakeholder value. The results show that the despite higher costs, biochemical cellulosic ethanol production has the maximum potential upside and acts as the backbone for the biorefinery. Additionally, an integrated utility generation facility and co-products streams, succinic acid and 1, 3-propanediol, improve the overall profit margins significantly. Furthermore, the co-products provide waste stream utilization opportunities which reduce the overall environmental load of the facility. Sensitivity analysis on the flexibility model indicates that biodiesel production and glycerol fermentation (to produce 1, 3-propanediol) offer tremendous design flexibility as their capacity design depend heavily on the realization of input parameters that affect them. Consequently incremental capacity design for these products can have a significant impact on the profitability of the entire biorefinery.

With mounting competitiveness in the energy sector it will be essential for a biorefining enterprise to assess accurately the options they have for raw material, technology, and product choices. Entry into any market will entail a large capital investment up front and the operation of the enterprise will evolve over time almost certainly with changing market dynamics. Consequently traditional approaches for project appraisal such as the NPV approach will fail to represent an accurate picture since they are static in nature and do not consider uncertainty in model parameters. Hence the systematic approach aforementioned can provide the firm with an essential tool that can be exploited to assess long-term decisions that the enterprise will have to “live-with” once they are made.