Green process design, green energy, and sustainability: A systems analysis perspective

This paper presents a systems analysis perspective that extends the traditional process design framework to green process design, green energy and industrial ecology leading to sustainability. For green process design this involves starting the design decisions as early as chemical and material selection stages on one end, and managing and planning decisions at the other end. However, uncertainties and multiple and conflicting objectives are inherent in such a design process. Uncertainties increase further in industrial ecology. The concept of overall sustainability goes beyond industrial ecology and brings in time dependent nature of the ecosystem and multi-disciplinary decision making. Optimal control methods and theories from financial literature can be useful in handling the time dependent uncertainties in this problem. Decision making at various stages starting from green process design, green energy, to industrial ecology, and sustainability is illustrated for the mercury cycling. Power plant sector is a major source of mercury pollution. In order to circumvent the persistent, bioaccumulative effect of mercury, one has to take decisions at various levels of the cycle starting with greener power systems, industrial symbiosis through trading, and controlling the toxic methyl mercury formation in water bodies and accumulation in aquatic biota.

[1]  R. A. Porter.  The Economics of Air Pollution , 1967 .

[2]  Urmila M. Diwekar,et al.  Introduction to Applied Optimization , 2020, Springer Optimization and Its Applications.

[3]  C. Gagnon,et al.  Bioavailability of inorganic and methylmercury to a marine deposit-feeding polychaete , 1998 .

[4]  Alan Krupnick,et al.  Economics of Pollution Trading for SO2 and NOx , 2005 .

[5]  J. Dales,et al.  Pollution, Property, and Prices , 1969 .

[6]  Y. Brodin,et al.  Liming of Acidified Surface Waters , 1995 .

[7]  U. Diwekar,et al.  Sustainable ecosystem management using optimal control theory: part 1 (deterministic systems). , 2006, Journal of theoretical biology.

[8]  Urmila M. Diwekar,et al.  An Efficient Sampling Approach to Multiobjective Optimization , 2004, Ann. Oper. Res..

[9]  John A. Sorensen,et al.  Airborne mercury deposition and watershed characteristics in relation to mercury concentrations in water, sediments, plankton, and fish of eighty Northern Minnesota lakes , 1990 .

[10]  L. Håkanson.  A general management model to optimize lake liming operations , 2003 .

[11]  János D. Pintér,et al.  Introduction to Applied Optimization , 2007, Eur. J. Oper. Res..

[12]  S. Rinaldi,et al.  Food chains in the chemostat: Relationships between mean yield and complex dynamics , 1998 .

[13]  Brian D. Fath,et al.  Towards a theory of sustainable systems , 2002 .

[14]  Charles T. Driscoll,et al.  The role of dissolved organic carbon in the chemistry and bioavailability of mercury in remote Adirondack lakes , 1995 .

[15]  J. M. Pratt,et al.  The kinetics and mechanism of cobalamin-dependent methyl and ethyl transfer to mercuric ion. , 1973, Biochimica et biophysica acta.

[16]  W. Montgomery,et al.  Markets in Licenses and Efficient Pollution Control Programs" Journal of Economic Theory , 1972 .

[17]  Helmut Wakeham.  Remarks on Chalmers W. Sherwin's Paper , 1965 .

[18]  S. Jensen,et al.  Biological Methylation of Mercury in Aquatic Organisms , 1969, Nature.

[19]  Urmila M. Diwekar,et al.  An efficient sampling technique for off-line quality control , 1997 .

[20]  P. Brezonik,et al.  Seasonal patterns of mercury species in water and plankton from softwater lakes in Northeastern Minnesota , 1998 .

[21]  M. Lydy,et al.  Toxicokinetics in aquatic systems: Model comparisons and use in hazard assessment , 1992 .

[22]  Harold Wolozin.  The economics of air pollution , 1966 .

[23]  B W Kooi,et al.  Food chain dynamics in the chemostat. , 1998, Mathematical biosciences.

[24]  Y Shastri,et al.  Sustainable ecosystem management using optimal control theory: part 2 (stochastic systems). , 2006, Journal of theoretical biology.

[25]  Urmila M. Diwekar,et al.  Stochastic maximum principle for optimal control under uncertainty , 2004, Comput. Chem. Eng..

[26]  M. Winfrey,et al.  Environmental factors affecting the formation of methylmercury in low pH lakes , 1990 .

[27]  Lars Håkanson,et al.  Presentation and analysis of a model simulating the pH response of lake liming , 1997 .

[28]  Tmdl At A Glance.  Total Maximum Daily Load (TMDL) for , 1999 .