Chemical enterprise model and decision-making framework for sustainable chemical product design

The chemical product substitution process is undertaken by chemical industries for complying with regulations, like REACH in Europe. Initially devoted to chemists, chemicals substitution is nowadays a complex process involving corporate, business and engineering stakeholders across the chemical enterprise for orienting the search toward a sustainable solution. We formalize a decision making process framework dedicated to the sustainable chemical product design activity in an industrial context. The framework aims at improving the sharing of information and knowledge and at enabling a collaborative work across the chemical enterprise stakeholders at the strategic, tactical and operational levels. It is supported by information and communication technologies (ICT) and integrates a computer aided molecular design tool. During the initial intelligence phase, a systemic analysis of the needs and usages enables to define the product requirements. In the design phase, they are compiled with the help of a facilitator to generate the input file of a computer aided product design tool. This multiobjective tool is designed to find mixtures with molecular fragments issued from renewable raw materials, and is able to handle environment-health and safety related properties along with process physicochemical properties. The final choice phase discusses the solution relevancy and provides feedback, before launching the product manufacturing. The framework is illustrated by the search of a bio-sourced water–solvent mixture formulation for lithographic blanket wash used in printing industry. The sustainability of the solution is assessed by using the sustainability shades metho

Review of Life Cycle Assessment in Agro-Chemical Processes

Life Cycle Assessment (LCA) is a method used to evaluate the potential impacts on the environment of a product, process, or activity throughout its life cycle. Today’s LCA users are a mixture of individuals with skills in different disciplines who want to evaluate their products, processes, or activities in a life cycle context. This study attempts to present some of the LCA studies on agro-chemical processes, recent advances in LCA and their application on food products and non-food products. Due to the recent development of LCA methodologies and dissemination programs by international and local bodies, use of LCA is rapidly increasing in agricultural and industrial products. The literatures suggest that LCA coupled with other environmental approaches provides much more reliable and comprehensive information to environmentally conscious policy makers, producers, and consumers in selecting sustainable products and production processes. For this purpose, a field study of LCA of biodiesel from Jatropha curcas has been taken as an example in the study. In the past, LCA has been applied primarily to products but recent literature suggests that it has also the potential as an analysis and design tool for processes and services. In general, all primary industries use energy and water resources and emit pollutants gases. LCA is a method to report on and analyze these resource issues across the life cycle of agro-chemical processes. This review has the importance as a first part of a research project to develop a life cycle assessment methodology for agro-chemical industries. It presents the findings of a literature review that focuses on LCA of agriculture and chemical engineering literatur

Computer aided framework for designing bio-based commodity molecules with enhanced properties

We investigate the use of computer aided molecular design (CAMD) approach for enhancing the properties of existing molecules by modifying their chemical structure to match target property values. The activity of tailoring molecules requires to aggregate knowledge disseminated across the whole chemical enterprise hierarchy, from the manager level to the chemists and chemical engineers, with different backgrounds and perception of what the ideal molecule would be. So, we propose a framework that allows the search to be successful in matching all requirements while capitalizing this knowledge spread among actors with different backgrounds with the help of SBVR (Semantics of Business Vocabulary and Rules) and OCL (Object Constraint Language). In the context of using biomass as the feedstock, we discuss the coupling of CAMD tools with computer aided organic synthesis tools so as to propose enhanced bio-sourced molecule candidates which could be synthesized with eco-friendly pathways. Finally, we evaluate the sustainability of the molecules and of the whole decision-process as well. Specific applications that concern the use of bio-sourced molecules are presented: a case of typical derivatives of chemical platform molecules issued from the itaconic acid to substitute N-methyl-2-pyrrolidone NMP or dimethyl-formamide DMF solvents and a case of derivatives of lipids to be used a biolubricants

What is Product-Service Systems (PSS)? A Review on PSS Researches and Relevant Policies

In order to achieve sustainable society, it is necessary to transform industrial structure to the one that does not reduce the Earth's resources. Under this circumstance, a business model of "not selling goods, but selling services" has been expected as a measure of co-existence of business and the environment. This idea, which is called as "Product-Service Systems: PSS" or "Servicizing" etc., has been studied in Europe, the United States and international organisations, and is now studied in Japan. However, the idea of PSS is still not effectively used for policy development.One of the major reasons is that PSS concept itself is under-developed. Under the unclear concept of PSS, researchers are working towards more scientific understanding while policy makers are trying to develop new policy measures, and there is confusion in those communities. In order to develop policy measures, it is necessary to make clear the position of PSS in socio-economic system. This paper overviews previous PSS researches and relevant policy measures conducted in Japan, the US and EU, and tries to grasp the context of researches and policy activities and to find out the agenda of the current status. The characteristics of PSS rest on the innovative relationship between producer and consumer. However, PSS researches are stuck at measurement of environmental loads, and relevant policies tend to be rest on the ones targeting producers. In order to get out of this situation, it is necessary to ask question what is PSS and to make it clear where PSS can be positioned in socio-economic system. PSS is important, because PSS has an element of creating sufficiency as well as eco-efficiency. It is recommended that PSS concept needs to be examined as a research effort, and environmentally sound product policy needs to be systematically organised

Optimal management of bio-based energy supply chains under parametric uncertainty through a data-driven decision-support framework

This paper addresses the optimal management of a multi-objective bio-based energy supply chain network subjected to multiple sources of uncertainty. The complexity to obtain an optimal solution using traditional uncertainty management methods dramatically increases with the number of uncertain factors considered. Such a complexity produces that, if tractable, the problem is solved after a large computational effort. Therefore, in this work a data-driven decision-making framework is proposed to address this issue. Such a framework exploits machine learning techniques to efficiently approximate the optimal management decisions considering a set of uncertain parameters that continuously influence the process behavior as an input. A design of computer experiments technique is used in order to combine these parameters and produce a matrix of representative information. These data are used to optimize the deterministic multi-objective bio-based energy network problem through conventional optimization methods, leading to a detailed (but elementary) map of the optimal management decisions based on the uncertain parameters. Afterwards, the detailed data-driven relations are described/identified using an Ordinary Kriging meta-model. The result exhibits a very high accuracy of the parametric meta-models for predicting the optimal decision variables in comparison with the traditional stochastic approach. Besides, and more importantly, a dramatic reduction of the computational effort required to obtain these optimal values in response to the change of the uncertain parameters is achieved. Thus the use of the proposed data-driven decision tool promotes a time-effective optimal decision making, which represents a step forward to use data-driven strategy in large-scale/complex industrial problems.Peer ReviewedPostprint (published version

A Comprehensive Optimization Framework for Designing Sustainable Renewable Energy Production Systems

As the world has recognized the importance of diversifying its energy resource portfolio away from fossil resources and more towards renewable resources such as biomass, there arises a need for developing strategies which can design renewable sustainable value chains that can be scaled up efficiently and provide tangible net environmental benefits from energy utilization. The objective of this research is to develop and implement a novel decision-making framework for the optimal design of renewable energy systems. The proposed optimization framework is based on a distributed, systematic approach which is composed of different layers including systems-based strategic optimization, detailed mechanistic modeling and operational level optimization. In the strategic optimization the model is represented by equations which describe physical flows of materials across the system nodes and financial flows that result from the system design and material movements. Market uncertainty is also incorporated into the model through stochastic programming. The output of the model includes optimal design of production capacity of the plant for the planning horizon by maximizing the net present value (NPV). The second stage consists of three main steps including simulation of the process in the simulation software, identification of critical sources of uncertainties through global sensitivity analysis, and employing stochastic optimization methodologies to optimize the operating condition of the plant under uncertainty. To exemplify the efficacy of the proposed framework a hypothetical lignocellulosic biorefinery based on sugar conversion platform that converts biomass to value-added biofuels and biobased chemicals is utilized as a case study. Furthermore, alternative technology options and possible process integrations in each section of the plant are analysed by exploiting the advantages of process simulation and the novel hybrid optimization framework. In conjunction with the simulation and optimization studies, the proposed framework develops quantitative metrics to associate economic values with technical barriers. The outcome of this work is a new distributed decision support framework which is intended to help economic development agencies, as well as policy makers in the renewable energy enterprises

A Modeling, Optimization, and Analysis Framework for Designing Multi-Product Lignocellulosic Biorefineries

The objective of this research is to propose a methodology to develop modular decision analysis frameworks to design value chains for enterprises in the renewable fuels and chemicals sector. The decision support framework focuses on providing strategic decision support to startup and new product ventures. The tasks that are embedded in the framework include process and systems design, technology and product selection, forecasting cost and market variables, designing network capacities, and analysis of risks. The Decision support system (DSS) proposed is based on optimization modeling; systems design are carried out using integer programming with multiple sets of process and network configurations utilized as inputs. Uncertainty is incorporated using real options, which are utilized to design network processing capacity for the conversion of biomass resources. Risk analysis is carried out using Monte Carlo methods. The DSS framework is exemplified using a lignocellulosic biorefinery case study that is assumed to be located in Louisiana. The biorefinery utilizes energy crops as feedstocks and processes them into cellulosic biofuels and biobased chemicals. Optimization modeling is utilized to select an optimal network, a fractionation technology, a fermentation configuration, and optimal product recovery and purification unit operations. A decision tree is then used to design incremental capacity under uncertain market parameters. The valuation methodology proposed stresses flexibility in decision making in the face of market uncertainties as is the case with renewable fuels and chemicals. The value of flexibility, termed as “Option Value” is shown to significantly improve the net present value of the proposed biorefinery. Monte Carlo simulations are utilized to develop risk curves for alternate capacity design plans. Risk curves show a favorable risk reward ratio for the case of incremental capacity design with embedded decision options. The framework proposed here can be used by enterprises, government entities and decision makers in general to test, validate, and design technological superstructures and network processing capacities, conduct scenario analyses, and quantify the financial impacts and risks of their representative designs. We plan to further add functionality to the DSS framework and make available the tools developed to wide audience through an “open-source” software distribution model