1133286 (Kirchain). This research will produce a systematic mapping of relative performance of deterministic and several forms of uncertainty-aware stochastic batch planning models for a range of production contexts. Specific contexts to be explored include at least three forms of batch quality performance functions (linear, power-law, and logarithmic) and four distributional forms of raw material (RM) quality (Lognormal, Max Extrema, Gamma, or Student's t). Additionally, this research will provide both analytical and quantitative case analysis that supports the economic and resource efficiency value of diversification of interdependence within industrial ecosystems. For three planned industrial case analyses, this research will quantify the distributional nature of RM quality and the current and potential ability to utilize secondary and renewable raw materials (SRRMs) while examining the impact of the number of raw materials attributes. The educational component of this research seeks to develop methods and case studies to incorporate sustainability into engineering education. These course materials will be integrated into graduate and undergraduate courses on industrial ecology and sustainable firm strategy taught by the PIs at MIT and RIT. The ultimate goal is to provide students with the knowledge-base to improve their engineering decisions by understanding how their decisions will impact society. The outcomes of these efforts will be shared with the broader academic community through forums such as the Center for Sustainable Production at RIT and MIT?s OpenCourseWare, a free and accessible web-based publication of much of MIT?s course content. The post-docs employed within this project will receive mentoring through a structured program to improve teaching, presentation, publication, and fund-raising skills. The broader impact of this work stems from the collaborations it necessitates between industry and academia; this collaboration enables real-world implementation tests to ensure that the research leads to actionable methods not just abstract concepts. The outcomes of the work will be widely disseminated to audiences outside of traditional academic communities because of the industrial partnerships employed in the research. Overall, the outcomes of the work are targeted to benefit society by developing tools that seek to reduce the environmental impact of the process industries and create sustainable material systems.
Global materials use is increasing at an unprecedented rate as both population and wealth continue to grow. This pace of increase is leading to difficulties in reliably meeting demand, coping with the emissions from materials extraction, and handling the large quantities of materials reaching end-of-life. Two strategies that will likely play a key role in addressing these challenges and moving towards more sustainable patterns of materials use are increasing the efficient use of (1) secondary (recycled) resources and (2) renewable (bio-derived) materials. Both strategies could delay depletion of non-renewable resources such as fossil fuels and avoid the deleterious effects of extraction. There are several significant barriers that limit the increased use of these materials including their common challenges: the heightened variation and uncertainty in the quality of recycled and renewable raw materials and a lack of clear information to allow decision-makers to select sustainable alternatives. This project aims to increase the use of recycled and bio-derived raw materials in a wide variety of process industries; particularly usage in construction and architectural applications. We have accomplished this goal by creating the knowledge and tools to allow recyclers and other materials producers to factor raw material quality uncertainty into their decisions and for designers to make more informed decisions about materials – allowing them to make more sustainable materials choices. Prior work by the principal investigators of this project has shown that it was possible for such planning tools (referred to as blending models) to allow recyclers to increase the use of recycled materials by more than 25%. However, that prior work was done for cases where the modeling of blending was most simple. In this project, these tools were extended to real world cases of aluminum recycling, bio-derived fuel production, and municipal-solid waste (MSW) recycling. From a modeling perspective these cases represent production environments where: a) there is no simple relationship between the quality of the raw materials and the quality of the product (bio-fuels), b) the variation in raw materials quality does not follow the "bell-curve" (i.e., normal distribution) which is so familiar (aluminum), and c) the production process involves multiple coordinated steps (MSW and novel approaches to aluminum recycling). Increasing the potential for use of recycled and bio-derived materials is not enough to drive sustainable materials use. To have impact, those materials must be selected by the market. Several green design guides, such as LEED for buildings, provide guidance on sustainable design. However, when designers and architects lack information on the real environmental benefit of alternative materials, they are unlikely to select them. Through a survey of green building programs, existing literature, and execution of several life-cycle assessments, this study tackled the question: do alternative building materials present a "greener" alternative? Through this, a general approach was developed for assessing types of alternative materials with useful connections to green building codes including LEED. Although this provides an important step towards sustainability-informed materials selection, the results of this study make clear that there is still a need for considerable more research to create an accurate picture of the true environmental impact of alternatives in various real-world contexts. In the end, this project has created the knowledge and tools to bring sustainable materials use closer to reality.
