A hybrid approach to prioritize risk mitigation strategies for biomass polygeneration systems

Abstract Biomass polygeneration system is one of the most attractive biomass technologies due to its technicality, feasibility and high associated investment returns. The synthesis, design and economic aspects of constructing a processing system using this technology are well-developed and have recently reached the stage of industrial implementation. Nonetheless, the early stage of technology development focuses on process and product safety and tends to ignore other risk aspects that are closely associated with the biomass value chain. Due to the complex nature of the biomass value chain, conventional risk mitigation strategies are ineffective in mitigating risks at the management level. More recent approaches, particularly stochastic programming methods, have yielded robust results in addressing technological risks and design uncertainties. However, such approaches are still unable to effectively consider non-quantitative risks such as business risks and regulatory risks. Hence, this study proposes a combined method of an analytical model and stochastic programming approach to prioritize risks and risk mitigation strategies for decision-making purposes. This work presents a novel multiple-criteria decision-making expert system based on fuzzy set theory, which is the Decision and Evaluation-based Fuzzy Analytic Network Process (DEFANP) method. The novel method functions to prioritize risk mitigation strategies within a network relationship of project goals, key components of the biomass industry and industrial stakeholders. As the stochastic risk mitigation counterpart, the fluctuations and uncertainties in operations, transportation, market supply-demand and price are modeled using the Monte Carlo simulation method. From this, risks of implementing biomass polygeneration systems can be mitigated by selecting a strategy that yields the highest analytical indicator while reconciling with the corresponding probabilities of achieving management goals. A palm biomass polygeneration system in Malaysia is presented as case study where the key implementation risks are regulatory risks, financing risks, technology risks, supply chain and feedstock risks, business risks, social and environmental risks.

[1]  F. P. Lees,et al.  HAZID, A COMPUTER AID FOR HAZARD IDENTIFICATION 1. The STOPHAZ Package and the HAZID Code: An Overview, the Issues and the Structure , 1999 .

[2]  Raymond R. Tan,et al.  A methodology for criticality analysis in integrated energy systems , 2015, Clean Technologies and Environmental Policy.

[3]  T Furuichi,et al.  A fuzzy analytic network process for multi-criteria evaluation of contaminated site remedial countermeasures. , 2008, Journal of environmental management.

[4]  Sotirios Karellas,et al.  Policy plan for the use of biomass and biofuels in Greece Part I: Available biomass and methodology , 2009 .

[5]  Denny K. S. Ng,et al.  Robust Optimization for Process Synthesis and Design of Multifunctional Energy Systems with Uncertainties , 2014 .

[6]  Mark G. Stewart,et al.  Risk assessment for civil engineering facilities: critical overview and discussion , 2003, Reliab. Eng. Syst. Saf..

[7]  Daren E. Daugaard,et al.  Techno-Economic Analysis of Biomass Fast Pyrolysis to Transportation Fuels , 2010 .

[8]  Suzana Yusup,et al.  Kinetics and thermodynamic analysis in one-pot pyrolysis of rice hull using renewable calcium oxide based catalysts. , 2018, Bioresource technology.

[9]  Satya-Lekh Proag,et al.  A Framework for Risk Assessment , 2014 .

[10]  Guangyin Xu,et al.  Polygeneration system and sustainability: Multi-attribute decision-support framework for comprehensive assessment under uncertainties , 2017 .

[11]  Dengyu Chen,et al.  Pyrolysis polygeneration of pine nut shell: Quality of pyrolysis products and study on the preparation of activated carbon from biochar. , 2016, Bioresource technology.

[12]  Colin Webb,et al.  A techno-economic comparison of Fischer-Tropsch and fast pyrolysis as ways of utilizing sugar cane bagasse in transportation fuels production. , 2017 .

[13]  Trevor A. Kletz,et al.  Hazop & Hazan: Identifying and Assessing Process Industry Hazards, Fouth Edition , 1999 .

[14]  T. Saaty,et al.  The Analytic Hierarchy Process , 1985 .

[15]  G. Büyüközkan,et al.  An integrated DEMATEL-ANP approach for renewable energy resources selection in Turkey , 2016 .

[16]  Mustafa Kamal,et al.  Waste-to-wealth: green potential from palm biomass in Malaysia , 2012 .

[17]  Carlo Menon,et al.  Environmental policies and risk finance in the green sector: Cross-country evidence , 2015 .

[18]  Andrew Dicks,et al.  Hydrogen from coal: Production and utilisation technologies , 2006 .

[19]  Panagiotis Grammelis,et al.  Process Integration of a Polygeneration Plant with Biomass/Coal Co-pyrolysis , 2017 .

[20]  Raymond R. Tan,et al.  A Fuzzy Analytic Hierarchy Process (fahp) Approach for Optimal Selection of Low-carbon Energy Technologies , 2015 .

[21]  Binti Mohd,et al.  Conventional and Microwave Pyrolysis of Empty Fruit Bunch and Rice Husk Pellets , 2017 .

[22]  Nevzat Şimşek,et al.  Recent incentives for renewable energy in Turkey , 2013 .

[23]  Yu Qian,et al.  Energetic/economic penalty of CO2 emissions and application to coal-to-olefins projects in China , 2015 .

[24]  William A. Smith,et al.  Understanding biomass feedstock variability , 2013 .

[25]  Min Liu,et al.  Using Last Planner and a Risk Assessment Matrix to Reduce Variation in Mechanical Related Construction Tasks , 2012 .

[26]  Eleftherios Iakovou,et al.  Biomass Supply Chain Management for Energy Polygeneration Systems , 2010 .

[27]  G. S. Vijaya Raghavan,et al.  Feedstocks, logistics and pre-treatment processes for sustainable lignocellulosic biorefineries: A comprehensive review , 2013 .

[28]  Prasanta Kumar Dey,et al.  Strategic sourcing in the UK bioenergy industry , 2013 .

[29]  Luis A. Ricardez-Sandoval,et al.  A survey on current advanced IGCC power plant technologies, sensors and control systems , 2014 .

[30]  Shihong Zhang,et al.  Biomass-based pyrolytic polygeneration system on cotton stalk pyrolysis: influence of temperature. , 2012, Bioresource technology.

[31]  Suhaidi Shafie,et al.  Current perspective of the renewable energy development in Malaysia , 2011 .

[32]  Choo Yuen May,et al.  Production and Characterization of Bio-Char from the Pyrolysis of Empty Fruit Bunches , 2011 .

[33]  A. Gabus,et al.  World Problems, An Invitation to Further Thought within the Framework of DEMATEL , 1972 .

[34]  İhsan Yüksel,et al.  A fuzzy analytic network process (ANP) model to identify faulty behavior risk (FBR) in work system , 2008 .

[35]  Poorna P. Ravula,et al.  Comparison between two policy strategies for scheduling trucks in a biomass logistic system. , 2008, Bioresource technology.

[36]  Hongmei Gu,et al.  Life-cycle GHG emissions of electricity from syngas produced by pyrolyzing woody biomass , 2015 .

[37]  Farhad Taghizadeh-Hesary,et al.  The way to induce private participation in green finance and investment , 2019 .

[38]  S. Talluri,et al.  Assessing the Efficiency of Risk Mitigation Strategies in Supply Chains , 2013 .

[39]  Ana Maria Dinu Risk in Financial Transactions and Financial Risk Management , 2014 .

[40]  S. Evans,et al.  Business Models and Supply Chains for the Circular Economy , 2018, Journal of Cleaner Production.

[41]  L. Wing,et al.  Risk Management Methods Applied to Renewable and Sustainable Energy: A Review , 2015 .

[42]  Raymond R. Tan,et al.  Target-oriented robust optimization of polygeneration systems under uncertainty , 2016 .

[43]  S. Mekhilef,et al.  A review on biomass as a fuel for boilers , 2011 .

[44]  F. Kari,et al.  Impacts of energy subsidy reform on the Malaysian economy and transportation sector , 2014 .

[45]  K. Khalili-Damghani,et al.  A novel hybrid MCDM approach for outsourcing supplier selection , 2016 .

[46]  Sachin Kumar Mangla,et al.  A Flexible Decision Framework for Building Risk Mitigation Strategies in Green Supply Chain Using SAP–LAP and IRP Approaches , 2014 .

[47]  Farhad Taghizadeh-Hesary,et al.  Why Is Green Finance Important? , 2019, SSRN Electronic Journal.

[48]  Kuntal Jana,et al.  Environmental impact of biomass based polygeneration - A case study through life cycle assessment. , 2017, Bioresource technology.

[49]  Petar Sabev Varbanov,et al.  Cleaner energy planning, management and technologies: Perspectives of supply-demand side and end-of-pipe management , 2016 .

[50]  Hamdan Hassan,et al.  Gasification of Triple Fuel Blends Using Pilot-Scale Fluidised-Bed Gasification Plant , 2014 .

[51]  E. Hambali,et al.  The Potential of Palm Oil Waste Biomass in Indonesia in 2020 and 2030 , 2017 .

[52]  M. Ekberg The Parameters of the Risk Society , 2007 .

[53]  John Brammer,et al.  Estimation of the production cost of fast pyrolysis bio-oil , 2012 .

[54]  Lidija Čuček,et al.  Carbon and nitrogen trade-offs in biomass energy production , 2012, Clean Technologies and Environmental Policy.

[55]  Hon Loong Lam,et al.  Sustainability evaluation for biomass supply chain synthesis: Novel principal component analysis (PCA) aided optimisation approach , 2018, Journal of Cleaner Production.

[56]  M. A. Mustafa,et al.  Project risk assessment using the analytic hierarchy process , 1991 .

[57]  Nitin Kumar,et al.  A review on biomass energy resources, potential, conversion and policy in India , 2015 .

[58]  Thomas L. Saaty,et al.  Decision making with dependence and feedback : the analytic network process : the organization and prioritization of complexity , 1996 .

[59]  Reza Kiani Mavi,et al.  Crude oil supply chain risk management with DEMATEL–ANP , 2015, Oper. Res..

[60]  Hon Loong Lam,et al.  Transportation decision tool for optimisation of integrated biomass flow with vehicle capacity constraints , 2016 .

[61]  Fengqi You,et al.  Design under uncertainty of hydrocarbon biorefinery supply chains: Multiobjective stochastic programming models, decomposition algorithm, and a Comparison between CVaR and downside risk , 2012 .

[62]  A. Vassallo,et al.  Polygeneration with biomass-integrated gasification combined cycle process: Review and prospective , 2018, Renewable and Sustainable Energy Reviews.

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

[64]  L. Webster Risk Mitigation Strategies , 2016 .

[65]  B. Wynne,et al.  Risk Society: Towards a New Modernity. , 1994 .

[66]  Litao Liu,et al.  Development Potentials and Policy Options of Biomass in China , 2010, Environmental management.

[67]  Prasanta Kumar Dey,et al.  A barrier and techno-economic analysis of small-scale bCHP (biomass combined heat and power) schemes in the UK , 2014 .

[68]  Andrew N. Arnette Renewable energy and carbon capture and sequestration for a reduced carbon energy plan: An optimization model , 2017 .

[69]  Efstratios N. Pistikopoulos,et al.  A Multi-Objective Optimization Approach to Polygeneration Energy Systems Design , 2010 .

[70]  Andrea Masini,et al.  The impact of behavioural factors in the renewable energy investment decision making process: Conceptual framework and empirical findings , 2012 .

[71]  M Sam Mannan,et al.  Fuzzy risk matrix. , 2008, Journal of hazardous materials.

[72]  Nicola Chiara,et al.  Financing renewable energy infrastructure: Formulation, pricing and impact of a carbon revenue bond , 2012 .

[73]  Mohammad Ataei,et al.  The application of fuzzy analytic hierarchy process (FAHP) approach to selection of optimum underground mining method for Jajarm Bauxite Mine, Iran , 2009, Expert Syst. Appl..

[74]  M. Browne,et al.  Logistics management and costs of biomass fuel supply , 1998 .

[75]  H. L. Lam,et al.  Integrating stakeholder's role in mitigating risks for future cleaner production , 2018 .

[76]  Shabbir H. Gheewala,et al.  Greenhouse gas savings potential of sugar cane bio-energy systems , 2010 .

[77]  Thomas A. Adams,et al.  Decomposition strategy for the global optimization of flexible energy polygeneration systems , 2012 .

[78]  Z. Salam,et al.  Malaysia's renewable energy policies and programs with green aspects , 2014 .

[79]  S. Si,et al.  DEMATEL Technique: A Systematic Review of the State-of-the-Art Literature on Methodologies and Applications , 2018 .

[80]  Raymond R. Tan,et al.  A stochastic fuzzy multi-criteria decision-making model for optimal selection of clean technologies , 2018 .

[81]  Raymond R. Tan,et al.  Multi-objective target oriented robust optimization for the design of an integrated biorefinery , 2018 .

[82]  Puan Yatim,et al.  Overview of the key risks in the pioneering stage of the Malaysian biomass industry , 2017, Clean Technologies and Environmental Policy.

[83]  M. Christopher,et al.  Building the Resilient Supply Chain , 2004 .

[84]  Xunpeng Shi,et al.  Economic, social and environmental impacts of fuel subsidies: A revisit of Malaysia , 2017 .

[85]  Dominik Röser,et al.  Sustainable utilisation of forest biomass for energy - possibilities and problems: policy, legislation, certification, and recommendations and guidelines in the Nordic, Baltic, and other European countries. , 2007 .

[86]  María del P. Pablo-Romero,et al.  Tax incentives to promote green electricity: An overview of EU-27 countries , 2010 .

[87]  Luiz Pinguelli Rosa,et al.  The declared barriers of the large developing countries waste management projects: The STAR model. , 2016, Waste management.

[88]  Jay H. Lee,et al.  Optimal design and global sensitivity analysis of biomass supply chain networks for biofuels under uncertainty , 2011, Comput. Chem. Eng..

[89]  G. Tzeng,et al.  Reconfiguring the innovation policy portfolios for Taiwan's SIP Mall industry , 2007 .

[90]  Hon Loong Lam,et al.  Financing green growth in malaysia: enabling conditions and challenges , 2017 .

[91]  Mu’azu Mohammed Abdullahi,et al.  DEMATEL-ANP Risk Assessment in Oil and Gas Construction Projects , 2017 .

[92]  Peteris Rivza,et al.  Risk assessment in renewable energy production from agriculture biomass in Latvia , 2012 .

[93]  Yi-Chung Hu,et al.  DEMATEL and Analytic Network Process for Evaluating Stock Trade Strategies Using Livermore's Key Price Logic , 2017 .