Risk Management in Product Design: Current State, Conceptual Model and Future Research

Risk management is an important element of product design. It helps to minimize the project- and product-related risks such as project budget and schedule overrun, or missing product cost and quality targets. Risk management is especially important for complex, international product design projects that involve a high degree of novel technology. This paper reviews the literature on risk management in product design. It examines the newly released international standard ISO 31000 “Risk management — Principles and guidelines” and explores its applicability to product design. The new standard consists of the seven process steps communication and consultation; establishing the context; risk identification; risk analysis; risk evaluation; risk treatment; and monitoring and review. A literature review reveals, among other findings, that the general ISO 31000 process model seems applicable to risk management in product design; the literature addresses different process elements to varying degrees, but none fully according to ISO recommendations; and that the integration of product design risk management with risk management of other disciplines, or between project and portfolio level in product design, is not well developed

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

Superstructure Optimization of Petroleum Refinary Design : Processing Alternatives for Vacuum GaS Oil Produced from Distillation Unit

The objective of this research project is to develop an optimization-based mathematical model in the form of a mixed-integer linear program (MILP) for determining the optimal configuration of a petroleum refinery. The optimization model serves as a decision support system taking into consideration the relevant engineering knowledge and the past design experience. The scope of work of this project focuses on the strategies and approaches for the optimal design of a petroleum refinery configuration for vacuum gas oil processing using mathematical programming in order to select the most economical and cost-efficient process route. The approach involves the development of a superstructure representation in which all feasible processing alternatives and routes for vacuum gas oil are considered. Subsequently, the associated constraints and a suitable cost minimization objective function are formulated to arrive at the optimal solution in terms of the optimal process units to be selected and the corresponding optimal flowrates of the material streams. Linear yield-based material balances are considered in the model constraints by obtaining representative yield values from the literature. Logical constraints in the form of logic cuts for stipulating the selection or no-selection of the process units (tasks) and material streams (states) are included in the optimization model formulation to further define the feasible region of solutions based on the relevant design specifications and the interconnectivity relationships among the nodes of states and tasks in the superstructure. Computational experiments are carried out by implementing the model on GAMS. A numerical example with a hypothetical set of product demands is illustrated, which is solved to optimality using GAMS, resulting in a practically-viable optimal refinery configuration that agrees well with real-world scenari

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

Risk Modelling and Simulation of Chemical Supply Chains using a System Dynamics Approach

A chemical supply chain (CSC) presents a network that integrates suppliers, manufacturers, distributors, retailers and customers into one system. The hazards arising from the internal system and the surrounding environment may cause disturbances to material, information and financial flows. Therefore, supply chain members have to implement a variety of methods to prepare for, respond to and recover from potential damages caused by different kinds of hazards. A large number of studies have been devoted to extending the current knowledge and enhancing the implementation of chemical supply chain risk management (CSCRM), to improve both safety and reliability of the CSCRM systems. However, the majority of existing risk management methods fail to address the complex interactions and dynamic feedback effects in the systems, which could significantly affect the risk management outcomes. In order to bridge the gaps, a new CSCRM method based on System Dynamics (SD) is proposed to accommodate the need to describe the connections between risks and their associated changes of system behaviour. The novelty of this method lies not only on providing a valid description of a real system, but also on addressing the interactions of the hazardous events and managerial activities in the systems. In doing so, the risk effects are quantified and assessed in different supply chain levels. Based upon the flexibility of SD modelling processes, the model developer can modify the developed model throughout the model life cycle. Instead of directly assessing different risks and providing arbitrary decisions, the obtained numerical results can offer supportive information for assessing potential risk reduction measures and continuously improving the CSC system performance. To demonstrate the applicability of the newly proposed method, a reputed specialty chemical transportation service provider in China is used and analysed through modelling and simulating the chemical supply chain transportation (CSCT) operations in various scenarios. It offers policy makers and operators insights into the risk-affected CSC operations and CSCRM decision-making processes, thus helping them develop rational risk reduction decisions in a dynamic environment

Portfolio Analysis in Supply Chain Management of a Chemicals Complex in Thailand

There is a considerable amount of research literature available for the optimisation of supply chain management of the chemical process industry. The context of supply chain considered in this thesis is the supply chain inside the chemical complex which is the conversion of raw materials into intermediate chemicals and finished chemical products through different chemical processes. Much of the research in the area of planning and scheduling for the process sector has been focused on optimising an individual chemical process within a larger network of a chemicals complex. The objective of this thesis is to develop a multi-objective, multi-period stochastic capacity planning model as a quantitative tool in determining an optimum investment strategy while considering sustainability for an integrated multi-process chemicals complex under future demand uncertainty using the development of inorganic chemicals complex at Bamnet Narong, Thailand as the main case study. Within this thesis, a number of discrete models were developed in phases towards the completion of the final multi-objective optimisation model. The models were formulated as mixed-integer linear programming (MILP) models. The first phase was the development of a multi-period capacity planning optimisation model with a deterministic demand. The model was able to provide an optimal capacity planning strategy for the chemicals complex at Bamnet Narong, Thailand. The numerical results show that based on the model assumptions, all the proposed chemical process plants to be developed in the chemicals complex are financially viable when the planning horizon is more than 8 years. The second phase was to build a multi-period stochastic capacity planning optimisation model under demand uncertainty. A three-stage stochastic programming approach was incorporated into the deterministic model developed in the first phase to capture the uncertainty in demand of different chemical products throughout the planning horizon. The expected net present value (eNPV) was used as the performance measure. The results show that the model is highly demand driven. The third phase was to provide an alternative demand forecasting method for capacity planning problem under demand uncertainty. In the real-world, the annual increases in demand will not be constant. A statistical analysis method named “Bootstrapping” was used as a demand generator for the optimisation model. The method uses historical data to create values for the future demands. Numerical results show that the bootstrap demand forecasting method provides a more optimistic solution. The fourth phase was to incorporate financial risk analysis as constraints to the previously developed multi-period three-stage stochastic capacity planning optimisation model. The risks associated with the different demand forecasting methods were analysed. The financial risk measures considered in this phase were the expected downside risk (EDR) and the mean absolute deviation (MAD). Furthermore, as the eNPV has been used as the usual financial performance measure, a decisionmaking method, named “Minimax Regret” was applied as part of the objective function to provide an alternative performance measure to the developed models. Minimax Regret is one kind of decision-making theory, which involves minimisation of the difference between the perfect information case and the robust case. The results show that the capacity planning strategies for both cases are identical Finally, the last phase was the development of a multi-objective, multi-period three stage stochastic capacity planning model aiming towards sustainability. Multiobjective optimisation allows the investment criteria to be traded off against an environmental impact measure. The model values the environmental factor as one of the objectives for the optimisation instead of this only being a regulatory constraint. The expected carbon dioxide emissions was used as the environmental impact indicator. Both direct and indirect emissions of each chemical process in the chemicals complex were considered. From the results, the decision-makers will be able to decide the most appropriate strategy for the capacity planning of the chemicals complex

Méthode multi-échelle pour la conception optimale d'une bioraffinerie multi-produit

De nos jours, de nouvelles technologies sont développées pour produire efficacement des produits dérivés de matières premières autresque le pétrole, comme par exemple la biomasse. En effet, la biomasse et plus spécifiquement la biomasse non alimentaire possède un fort potentielcomme substitut aux ressources fossiles pour des raisons environnementales, économiques et politiques. Dans ce contexte, l’étude des bioraffineries offre de nouvelles opportunités pour le Process System Engineering et plus particulièrement pour des activités de recherche quivisent la conception de systèmes constitués d’entités interconnectés. En effet, le verrou principal se concentre sur la modélisation et l’optimisation multi-échelle de la bioraffinerie qui permet l’intégration de plusieurs échelles spatiales allant de l’échelle moléculaire à celle de l’unité de production. Ces différentes échelles sont essentielles pour décrire correctement le système puisqu’elles interagissent en permanence. La forte dilution des courants est le meilleur exemple pour illustrer ces interactions. En effet, la présence d’eau induit de nombreux problèmes thermodynamiques (azéotropes, etc.) à l’échelle moléculaire, ce qui impacte fortement la topologie du procédé notamment sur les étapes de séparation, de purification et detraitement des purges (pour limiter les pertes en produits). Ainsi, la performance de la séquence d’opérations unitaires de l’étape de purification dépend entièrement de la concentration en eau. De plus dans la conception de bioraffinerie, il est fréquent de coupler fermentation et séparation afin d’améliorer les performances de la fermentation et de limiter la présence d’eau dans l’étapede purification. Par ailleurs, la grande quantité d’eau à chauffer ou refroidir entraine la nécessité de réaliser l’intégration énergétique du réseaud’échangeurs du procédé afin de minimiser le coût les dépenses énergétiques. L’objectif de ce travail est alors de proposer une méthodologie générique et les outils associés afin de lever certains verrous de la modélisation et l’optimisation multi-échelle de la bioraffinerie. Basée sur une approche par superstructure, la finalité de la méthodologie est d’évaluer les performances des alternatives étudiées en termes technico-économiques, environnementaux et d’efficacité énergétique en vue de son optimisation multi-objectifs pour trouver la voie de traitement optimale pour le(s) bioproduit(s) d’intérêt. Le cas d’application retenu se focalise sur la production de biobutanol à partir du système Acétone-Butanol-Ethanolet d’une biomasse d’origine forestière. La première étape de la méthodologie proposée concerne la création de la superstructure de la bioraffineriebasée sur une décomposition de cette dernière en 5 étapes principales : le prétraitement, la fermentation, la séparation, la purification et letraitement des purges. Ensuite, la seconde étape consiste à modéliser chaque alternative de procédé. Cette modélisation utilise un modèlethermodynamique à coefficients d’activité afin de décrire le comportement fortement non-idéal des molécules du milieu. De plus, l’intégration du traitement des purges et de l’intégration énergétique durant cette étape permet d’améliorer le procédé. Enfin, la dernière étape s’intéresse à l’optimisation multiobjectif qui se focalise sur différents aspects : maximisation de la production, minimisation des coûts, du prix minimal de vente des bioproduits, des pertes en produits et de l’impact environnemental. Cette dernière étape inclut également des études de sensibilité sur les différents paramètres de la méthodologie : opératoires, économiques, environnementaux... A l’issu de l’optimisation, un compromis seratrouvé afin d’obtenir une bioraffinerie durable