Induction approach via P-Graph to rank clean technologies

Identification of appropriate clean technologies for industrial implementation requires systematic evaluation based on a set of criteria that normally reflect economic, technical, environmental and other aspects. Such multiple attribute decision-making (MADM) problems involve rating a finite set of alternatives with respect to multiple potentially conflicting criteria. Conventional MADM approaches often involve explicit trade-offs in between criteria based on the expert's or decision maker's priorities. In practice, many experts arrive at decisions based on their tacit knowledge. This paper presents a new induction approach, wherein the implicit preference rules that estimate the expert's thinking pathways can be induced. P-graph framework is applied to the induction approach as it adds the advantage of being able to determine both optimal and near-optimal solutions that best approximate the decision structure of an expert. The method elicits the knowledge of experts from their ranking of a small set of sample alternatives. Then, the information is processed to induce implicit rules which are subsequently used to rank new alternatives. Hence, the expert's preferences are approximated by the new rankings. The proposed induction approach is demonstrated in the case study on the ranking of Negative Emission Technologies (NETs) viability for industry implementation.

[1]  Gareth Johnson,et al.  Negative emissions technologies and carbon capture and storage to achieve the Paris Agreement commitments , 2018, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[2]  Raymond R. Tan,et al.  P-graph approach for GDP-optimal allocation of resources, commodities and capital in economic systems under climate change-induced crisis conditions , 2015 .

[3]  G. Rau,et al.  Electrochemical splitting of calcium carbonate to increase solution alkalinity: implications for mitigation of carbon dioxide and ocean acidity. , 2008, Environmental science & technology.

[4]  L. T. Fan,et al.  Combinatorially Accelerated Branch-and-Bound Method for Solving the MIP Model of Process Network Synthesis , 1996 .

[5]  Raymond R. Tan,et al.  Prospects and challenges for chemical process synthesis with P-graph , 2019 .

[6]  John C. Mankins,et al.  Technology readiness assessments: A retrospective , 2009 .

[7]  Theresa Beaubouef,et al.  Rough Sets , 2019, Lecture Notes in Computer Science.

[8]  Lukas H. Meyer,et al.  Summary for Policymakers , 2022, The Ocean and Cryosphere in a Changing Climate.

[9]  Raymond R. Tan,et al.  Optimal human resource planning with P-graph for universities undergoing transition , 2019, Journal of Cleaner Production.

[10]  Atul K. Jain,et al.  Global Carbon Budget 2018 , 2014, Earth System Science Data.

[11]  Rafael Bello,et al.  Rough Sets in Machine Learning: A Review , 2017 .

[12]  L. T. Fan,et al.  Graph-theoretic approach to process synthesis: axioms and theorems , 1992 .

[13]  Kathleen B. Aviso,et al.  Optimizing Human Resource Allocation in Organizations During Crisis Conditions: a P-graph Approach , 2017 .

[14]  Ferenc Friedler,et al.  Combinatorial algorithms for process synthesis , 1992 .

[15]  Raymond R. Tan,et al.  Fuzzy AHP approach to selection problems in process engineering involving quantitative and qualitative aspects , 2014 .

[16]  Nilay Shah,et al.  High-level techno-economic assessment of negative emissions technologies , 2012 .

[17]  W. Pedrycz,et al.  A fuzzy extension of Saaty's priority theory , 1983 .

[18]  Ravi Paul,et al.  Analyzing the structure of expert knowledge , 2006, Inf. Manag..

[19]  Raymond R. Tan,et al.  Application of rough sets for environmental decision support in industry , 2008 .

[20]  Christian Fonteix,et al.  Multicriteria optimization of a high yield pulping process with rough sets , 2003 .

[21]  Dominic C.Y. Foo,et al.  P-graph and Monte Carlo simulation approach to planning carbon management networks , 2017, Comput. Chem. Eng..

[22]  L. T. Fan,et al.  Graph-theoretic approach to process synthesis: Polynomial algorithm for maximal structure generation , 1993 .

[23]  Raymond R. Tan,et al.  Towards generalized process networks: prospective new research frontiers for the p-graph framework , 2018 .

[24]  Duncan McLaren,et al.  A comparative global assessment of potential negative emissions technologies , 2012 .

[25]  Botond Bertók,et al.  A Graph-theoretic Method to Identify Candidate Mechanisms for Deriving the Rate Law of a Catalytic Reaction , 2002, Comput. Chem..