In chemical and allied industries, process design sustainability has gained public concern in academia, industry, government agencies, and social groups. Over the past decade, a variety of sustainability indicators have been introduced, but with various challenges in application. It becomes clear that the industries need urgently practical tools for conducting systematic sustainability assessment on existing processes and/or new designs and, further, for helping derive the most desirable design decisions. This paper presents a systematic, general approach for sustainability assessment and design selection through integrating hard (quantitative) economic and environmental indicators along with soft (qualitative) indicators for social criteria into design activities. The approach contains four modules: a process simulator module, an equipment and inventory acquisition module, a sustainability assessment module, and a decision support module. The modules fully utilize and extend the capabilities of the process simulator Aspen Plus, Aspen Simulation Workbook, and a spreadsheet, where case model development, data acquisition and analysis, team contribution assessment, and decision support are effectively integrated. The efficacy of the introduced approach is illustrated by the example of biodiesel process design, where insightful sustainability analysis and persuasive decision support show its superiority over commonly practiced technoeconomy evaluation approaches.

In the design of chemical/energy production systems, a major challenge is how to quantify the sustainability of the systems. Concerns on economic return and environmental impacts have been well received by researchers and practitioners. However, the irreversibility of the process has not been taken into consideration yet. Based on the first and second laws of thermodynamics, exergy analysis allows accounting for irreversibility in the process and provides a detailed mechanism for tracking the transformation of energy and chemicals. Sustainability assessment in the societal dimension is mostly a “soft” activity, as the aspects to be considered and the method of evaluation are frequently subjective. How to assess the societal impact of a process in the early design stage remains as a challenging issue. This paper will present a sustainability assessment method incorporating economic, environmental, efficiency, and societal concerns. The efficiency assessment is conducted through exergy analysis, while the societal concerns are measured by an enhanced inherent safety index method. In conjunction with a multicriteria decision-analysis method, this methodology will provide critical guidance to the designers. The efficacy of this methodology will be demonstrated through a case study on biodiesel production processes. The results show that the new heterogeneous catalyst process performs better than the traditional homogeneous process in every dimension.

Technology‐based sustainability enhancement is a key approach for industrial sustainability realization. However, identification of effective technologies for any industrial system could be very challenging. If the available data and information about the industrial system and technologies are incomplete, imprecise, and uncertain, then technology identification could be very difficult. In this article, the authors introduce a simple, yet systematic interval‐parameter‐based methodology for identifying quickly superior solutions under uncertainty for sustainability performance improvement. The methodology is general enough for the study of sustainability enhancement problems of any size and scope. A case study on sustainable development of biodiesel manufacturing demonstrates methodological efficacy. © 2012 American Institute of Chemical Engineers AIChE J, 58: 1841–1852, 2012

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Clearcoat filmbuild is one of the main concerns for painting quality in automotive paint shops. In operation, clearcoat is sprayed on the vehicle surface and then baked in an oven where solvents are removed and cross-linking reactions take place. Because of operational complexity and measurement difficulty, the process dynamics is basically unknown. Thus, the operational settings in the oven are solely experience-based. Hitherto, the studies on automotive oven operations has been only at the lab level. To significantly improve clearcoat filmbuild, a model at the plant floor level should be developed. In the present work, a first-principles-based, simplified, integrated dynamic model is developed to characterize the clearcoat reactive drying process in an oven. It is demonstrated that the model can provide satisfactory long-term predictions of filmbuild, facilitate a thorough analysis of drying/curing operations, and form the basis for developing optimal operational strategies for improving film quality and reducing defects.

In the last few decades, heat exchanger networks have been widely adopted to recover thermal energy. Recently, mass exchanger networks have gained attention for recovering useful materials. By analogy, another class of exchanger networks, termed work exchanger networks (WEN's), is conceivable for recovering mechanical energy. In the present work, the concept of the WEN is introduced. The WEN consists of a number of work exchangers, each of which serves to exchange mechanical energy between a high-pressure stream and a low-pressure stream. By applying the basic principles of energy balance and thermodynamics laws, the necessary and sufficient conditions are derived to identify feasible stream matching in work exchangers. A comprehensive analysis of stream match mode gives rise to various heuristic rules useful for WEN synthesis. Our focus is on the analysis, rather than the synthesis, of the WEN. This work lays a foundation to the development of a systematic WEN synthesis methodology.

Using optimization based methods to predict fluxes in metabolic flux balance models has been a successful approach for some microorganisms, enabling construction of in silico models and even inference of some regulatory motifs. However, this success has not been translated to mammalian cells. The lack of knowledge about metabolic objectives in mammalian cells is a major obstacle that prevents utilization of various metabolic engineering tools and methods for tissue engineering and biomedical purposes. In this work, we investigate and identify possible metabolic objectives for hepatocytes cultured in vitro. To achieve this goal, we present a special data-mining procedure for identifying metabolic objective functions in mammalian cells. This multi-level optimization based algorithm enables identifying the major fluxes in the metabolic objective from MFA data in the absence of information about critical active constraints of the system. Further, once the objective is determined, active flux constraints can also be identified and analyzed. This information can be potentially used in a predictive manner to improve cell culture results or clinical metabolic outcomes. As a result of the application of this method, it was found that in vitro cultured hepatocytes maximize oxygen uptake, coupling of urea and TCA cycles, and synthesis of serine and urea. Selection of these fluxes as the metabolic objective enables accurate prediction of the flux distribution in the system given a limited amount of flux data; thus presenting a workable in silico model for cultured hepatocytes. It is observed that an overall homeostasis picture is also emergent in the findings.