Successful design of industrial products and processes requires consideration of the complex interactions between economic, environmental and social factors that span multiple spatial and temporal scales. For example, engineering design decisions are made at a fine scale of individual equipment and process, while its implications are felt at much coarser scales of the economy and ecosystems. In contrast, policies are designed at a coarse scale, but its effects are felt at finer scales. Most methods for engineering design and policy analysis tend to focus on one primary scale. For example, life cycle assessment focuses mainly on the scale of the value chain, while economic input-output, and general and partial equilibrium models focus on a macroeconomic scale. Therefore integrated multiscale modeling of biocomplexity is required to analyze the complex, dynamic, and unpredictable effect of decisions from multiple disciplinary. The U.S. National Science Foundation has defined biocomplexity as follows: “Biocomplexity refers to the dynamic web of often surprising interrelationships that arise when components of the global ecosystem- biological, physical, chemical, and the human dimension –interact”. Analyzing these interrelations among the multiple spatial and temporal scales ensures the feasibility of the industrial process design and includes the broader implications on the economy, environment and society. An approach for integrating economic models with engineering process models will be described and the framework for the optimization of a production plant (finer scale) with the consideration of the policy impact (carbon tax) from the economy (coarser scale) is illustrated with a case study. A coal fired electricity generation plant is introduced to depict the finer scale engineering model that captures the optimized engineering variables subject to the material and energy balances, demands of steam and power, and other restrictions on the system. And the Leontief price input-output model is integrated to the engineering model to capture the change of the commodities prices by the change of exogenous policy (carbon tax) from the coarser economic scale. Some data such as economic input-output benchmark data from the U.S. Bureau of Economic Analysis (BEA) and some energy related data from Energy Information Administration (EIA) are utilized in order to integrate the feedback effect of the price change in the economy level into the engineering model. The objective of the multiscale integrated model is to maximize the profit of the production plant by the choice of commodities price change, different steam flow rates, the power generated and the purchased power. Several different scenarios are designed to compare the effect of the single and multi scale modeling for the optimized process design of the production plant. Different economic models will be integrated with the engineering model to compare the advantage and disadvantage of each model. New challenges and areas of further research will be identified, including opportunities for integrating life cycle analysis with economic modeling. A general multiscale stochastic modeling framework will be proposed for reaching these goals.

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