Intensive Data and Knowledge-Driven Approach for Sustainability Analysis: Application to Lignocellulosic Waste Valorization Processes

The use of circular economy is becoming more and more important, particularly in the field of agriculture, a major provider of waste. In particular, a lot of researches are being done to transform the lignocellulosic waste from agriculture through desired "sustainable" processes. Sustainable processes mean economically viable, socially accepted, and environmentally responsible processes. Thanks to the "life cycle thinking", it is possible to assess such potential environmental impacts. However, these environmental analyzes require a lot of specific data, whose collection can be long and tedious, or simply impossible in practice. On the other hand, the huge amount of scientific articles describing the processes of valorization of co-products of agriculture constitutes a great, largely under-exploited source of data. Knowledge engineering (KE) tools can be used to compile processes and analyze them. In this paper, we propose an innovative approach, based on intensive data and KE methods, to help a decision maker to choose between different pretreatment processes and different biomasses. The main goal is to develop an intensive, semi-automated data collection approach and an associated tool for assistance with choices in a circular economy context. It is defined by five steps: (1) goal and scope, (2) intensive data and knowledge and the allocation of flows and releasesstructuration and integration, (3) life cycle inventory (LCI), (4) sustainability assessment and (5) analysis and ranking. The study of 13 pretreatment processes of rice straw and corn stover validate our proposal.

[1]  Alon Y. Halevy,et al.  Principles of Data Integration , 2012 .

[2]  N. Mueller,et al.  Leverage points for improving global food security and the environment , 2014, Science.

[3]  Chang-Shing Lee,et al.  Intelligent estimation agent based on CMMI ontology for project planning , 2008, 2008 IEEE International Conference on Systems, Man and Cybernetics.

[4]  Ulf-Dietrich Reips,et al.  "Big Data" : big gaps of knowledge in the field of internet science , 2012 .

[5]  Hans-Jörg Althaus,et al.  The ecoinvent Database: Overview and Methodological Framework (7 pp) , 2005 .

[6]  John W. Sutherland,et al.  LCA-oriented semantic representation for the product life cycle , 2015 .

[7]  Denis Kouame,et al.  New Estimators and Guidelines for Better Use of Fetal Heart Rate Estimators with Doppler Ultrasound Devices , 2014, Comput. Math. Methods Medicine.

[8]  Helen Kopnina,et al.  Green-washing or best case practices? Using circular economy and Cradle to Cradle case studies in business education , 2019, Journal of Cleaner Production.

[9]  R. Clift,et al.  Developing a sustainability framework for the assessment of bioenergy systems , 2007 .

[10]  David Dornfeld,et al.  Data-intensive Life Cycle Assessment (DILCA) for Deteriorating Products , 2015 .

[11]  Nguyen Truong Son,et al.  A Pattern Approach for Biomedical Event Annotation , 2011, BioNLP@ACL.

[12]  C. Vialle,et al.  A circular economy and industrial ecology toolbox for developing an eco-industrial park: perspectives from French policy , 2019, Clean Technologies and Environmental Policy.

[13]  Joyce Smith Cooper,et al.  Big Data in Life Cycle Assessment , 2013 .

[14]  Elena Paslaru Bontas Simperl,et al.  ONTOCOM: A reliable cost estimation method for ontology development projects , 2012, J. Web Semant..

[15]  Liliana Ibanescu,et al.  Fuzzy Web Data Tables Integration Guided by an Ontological and Terminological Resource , 2013, IEEE Transactions on Knowledge and Data Engineering.

[16]  Gjalt Huppes,et al.  Methods for Life Cycle Inventory of a product , 2005 .

[17]  Deyu Zhou,et al.  Biomedical Relation Extraction: From Binary to Complex , 2014, Comput. Math. Methods Medicine.

[18]  Sai Liang,et al.  Big Data and Industrial Ecology , 2015 .

[19]  Thierry Marchant,et al.  Evaluation and Decision Models with Multiple Criteria: Stepping Stones for the Analyst , 2006 .

[20]  Evert van der Heide,et al.  Alkaline twin-screw extrusion pretreatment for fermentable sugar production , 2013, Biotechnology for Biofuels.

[21]  Haijiang Li,et al.  An ontology-based approach supporting holistic structural design with the consideration of safety, environmental impact and cost , 2018, Adv. Eng. Softw..

[22]  M. Taherzadeh,et al.  Agricultural, Industrial, Municipal, and Forest Wastes , 2019, Sustainable Resource Recovery and Zero Waste Approaches.

[23]  Natalya F. Noy,et al.  Semantic integration: a survey of ontology-based approaches , 2004, SGMD.

[24]  Jeroen B. Guinee,et al.  Handbook on life cycle assessment operational guide to the ISO standards , 2002 .

[25]  Mark A. J. Huijbregts,et al.  ReCiPe2016: a harmonised life cycle impact assessment method at midpoint and endpoint level , 2016, The International Journal of Life Cycle Assessment.

[26]  Ronald S. Zalesny,et al.  Pretreatment of woody biomass for biofuel production: energy efficiency, technologies, and recalcitrance , 2010, Applied Microbiology and Biotechnology.

[27]  Liliana Ibanescu,et al.  An Ontological and Terminological Resource for n-ary Relation Annotation in Web Data Tables , 2011, OTM Conferences.

[28]  S. Ulgiati,et al.  A review on circular economy: the expected transition to a balanced interplay of environmental and economic systems , 2016 .

[29]  A. A. Burgess,et al.  Application of life cycle assessment to chemical processes , 2001 .

[30]  Not Indicated,et al.  International Reference Life Cycle Data System (ILCD) Handbook - General guide for Life Cycle Assessment - Provisions and Action Steps , 2010 .

[31]  Sébastien Destercke,et al.  Evaluating Data Reliability: An Evidential Answer with Application to a Web-Enabled Data Warehouse , 2013, IEEE Transactions on Knowledge and Data Engineering.

[32]  Chris Davis,et al.  Secondary Resources in the Bio-Based Economy: A Computer Assisted Survey of Value Pathways in Academic Literature , 2017, Waste and Biomass Valorization.

[33]  Sébastien Destercke,et al.  A decision support system for eco-efficient biorefinery process comparison using a semantic approach , 2016, Comput. Electron. Agric..

[34]  Michael Betz,et al.  Using GaBi 3 to perform life cycle assessment and life cycle engineering , 2001 .

[35]  Li Zhou,et al.  An ontology framework towards decentralized information management for eco-industrial parks , 2018, Comput. Chem. Eng..

[36]  Hao Yu,et al.  Discovering patterns to extract protein-protein interactions from the literature: Part II , 2005, Bioinform..

[37]  A. Barakat,et al.  Dry fractionation process as an important step in current and future lignocellulose biorefineries: a review. , 2013, Bioresource technology.

[38]  Matthias Fischer,et al.  Effects on Life Cycle Assessment — Scale Up of Processes , 2007 .

[39]  B. Kulkarni,et al.  Environmental Impact Study of Bagasse Valorization Routes , 2019 .

[40]  Hajo Rijgersberg,et al.  How semantics can improve engineering processes: A case of units of measure and quantities , 2011, Adv. Eng. Informatics.

[41]  Hamid Zilouei,et al.  Organosolv pretreatment of rice straw for efficient acetone, butanol, and ethanol production. , 2014, Bioresource technology.

[42]  Juliette Dibie,et al.  [MS]^2O - A Multi-scale and Multi-step Ontology for Transformation Processes: Application to Micro-Organisms , 2016, ICCS.

[43]  Abdellatif Barakat,et al.  Innovative combined dry fractionation technologies for rice straw valorization to biofuels , 2015 .

[44]  I. Bateman,et al.  Sustainable Intensification in Agriculture: Premises and Policies , 2013, Science.

[45]  Sala Serenella,et al.  Supporting information to the characterisation factors of recommended EF Life Cycle Impact Assessment methods: New methods and differences with ILCD , 2018 .

[46]  Lenny Koh,et al.  An integrated theoretical framework to enhance resource efficiency, sustainability and human health in agri-food systems , 2016 .