(194g) Control Theory Applications for the Life Cycle Assessment of Improved Industrial Sustainability

The concept of sustainability is often associated with the statement from the World Commission on Environment and Development in 1987: “… development that meets the needs and aspirations of the present without compromising the ability to meet those of the future …”[1] Industrial sustainability is a vital issue aimed towards pursuing the long-term development of a given industry, which is closely related to the material efficiency of an industrial zone, region, or beyond. Despite comprehensive concerns and considerable efforts toward sustainability, many industrial activities have profound impacts not only on people's quality of life, but also to the global environment and economy. Industrial sustainability is, therefore, a very important issue in which the improvement of the efficiency of material and energy usage becomes beneficial to the sustainable development of an industrial system. Such improvements include the reduction of raw material consumption and/or waste generation, while simultaneously maintaining previous production levels.

This paper looks to introduce novel sustainability metrics, which are drawn from particular elements of process control theory and statistics, for the life cycle analysis of industrial example problems (e.g., plastic flow through the automotive industry). The life cycle of the industrial examples we look to analyze include raw material extraction, material processing, product fabrication, product use, post-product processing, and residue disposal, a true cradle to grave assessment. The study is quite valuable in evaluating the overall sustainability of a given industry, manufacturing plant, or region. Multiple models (e.g. control loops) will be generated based on the selection of the state, manipulated, and disturbance variables of the system. Analysis of these models using properties such as their eigenvalues, singular values, and condition numbers, can give forecasters insight as to how quickly the industry would be able to response to changes in raw material availability, consumer demand, and threshold quantities for chemical waste generation. Additionally, this method provides the capability of predicting the system's sensitivity to errors in data. An accurate system condition number requires less data from the industry, while still gaining full comprehension of the industry trends.

This area of research is quite valuable to the area of sustainability due to its novel and concise nature of problem description and evaluation. The metrics implemented allow industry forecasters to predict the system response to fluctuations of system inputs or changes in government regulations of system outputs. This information can then be used to adapt and modify the industry dependencies and determine alternative methods of product manufacturing if necessary. This methodology is also useful for industry forecasters to analyze potential industry changes as well as evaluate their effects on the industry's sustainability in the future.

[1] World Commission on Environment and Development, Our Common Future, Oxford University Press, Oxford, 1987.