Consequence modeling and analysis of explosion and fire hazards caused by methane emissions in a refinery in cold and hot seasons

Abstract Background and Aim: Methane is one of the gaseous materials with high potential of damage. Today, it is widely used in process, chemical industries and human environments. This study thus aimed to predict the emissions and the probable effects of liquid methane using ALOHA software in order to perform appropriate safety measures and consequently to reduce the plausible adverse effects. Materials and Methods: Considering the results of HAZOP studies, the worst case scenario was chosen and, using ALOHA software, possible methane gas leak scenarios from the reservoir were modeled. During the study, all the moral standards were observed. Results: Based on the results obtained, gas concentrations of liquid methane would reach 400000 ppm in a distance of 39 meters around the reservoir, which is in the range of PAC-3 demonstrating risk of death threatening the lives of surrounding people. In the event of a full leakage of 238 meters around the reservoir, the methane gas concentration is predicted to be 50000 ppm, which is equivalent to the low explosive charge (LEL) of methane gas. Wave pressure of vapor cloud caused by methane leaks exceeds 1 psi in a distance of 270 meters. Conclusion: The consequences of methane toxicity in the studied refinery are one of the most serious threats to the personnel. Therefore, preparing a reaction plan for emergency conditions will have an effective role in limiting the harmful effects of the toxic and dangerous materials emissions. Keywords: Methane gas, consequence modeling, refinery, ALOHA softwar

Re-distributed Manufacturing (RdM) Studio: Simulation Model Development

[EN] Consumer Goods Industry has gone through significant changes during the last years. A challenging economic climate, advances in technology and shifts in the consumer¿s attitude have led manufacturers to transform their operating models. Re-Distributed Manufacturing (RdM) aims to address these changes moving towards smaller-scale local manufacture to create a more resilient and connected system, providing not only an agile, user-driven approach that will allow for personalisation and customisation for Product-Service Systems (PSS); but also sustainability through the circular economy. This research aims to develop a simulation environment based on a current RdM business model, also predicting a future RdM business model based on data-driven decisions. Thus, the model has been employed to compare existing and future RdM scenarios to quantify and spot potential benefits of future RdM models. To achieve this, a System Dynamics Simulation has been built. For this study, changing input parameters regarding recyclability, transportation, the level of automation and level of servitization has been the way of representing the future that RdM will bring to this particular case; showing their impact on operating costs and service efficiency. The SD business simulation has been validated by experts and is a good example of how data-driven experimentation can predict the future of RdM, with the parameters and variables selected being critical for the model. The simulation model produced by this research showed promising results: operating costs reduced by 40%, PSS revenues in 6 months and immediate response of the system to customer demand.Rivas Pizarroso, JL. (2016). Re-distributed Manufacturing (RdM) Studio: Simulation Model Development. Universitat Politècnica de València. http://hdl.handle.net/10251/144650TFG

A system dynamics simulation model for environmental risk assessment at strategic level in power plants

YesIn a constantly changing business environment, a systematic approach is needed for risk assessment in order to allow for a more long-term strategic view. The System Dynamics (SD) modelling technique can be applied as an effective approach to understand the dynamic behaviour of a system over time. This understanding can be subsequently explicitly reflected on policies, strategic plans and operational procedures. This paper presents a SD model to assess environmental risks in power plants. The model helps to understand the long-term behaviour of the system under study. A questionnaire and focus group interviews have been conducted to understand the relationship among various risks. The SD model has been validated with two power plants in the Middle East. The developed model highlighted the impact of environmental risks on the performance of power plants. Although the SD model focuses on risk assessment in power plants, it can be easily adapted to other industry sectors

Shifting Orders Among Suppliers Considering Risk, Price And Transportation Cost

Order allocation for supplier is an important decision for an enterprise to realize a sustainable production. It was related to the suppliers function as a raw material provider and other supporting materials that will be used in production process. Initially most of previous research only focus on system analysis of order allocation supplier without doing analysis of risk and overall supply chain cost. Problem encountered in this research is to determine shifting order among suppliers that considering risk and transportation cost for single commodity multi supplier. The supply chain risk management process is investigated and a procedure was proposed in the risk mitigation phase as a form of risk profile. In this research model is proposed an initial procurement plan by using linear programming and also is revised the first optimal solution by including the risk profile factor. The objective of analysis risk profile in order allocation is to maximize the product flow from a risky supplier to a relatively less risky supplier. This supply chain risk management procedure including this proposed procedure is applied to a sugar company. The model is able to demonstrate that the result are different than the initial model.The result suggested that order allocations should be maximized in suppliers that have a relatively less risk profile value and minimized on suppliers that have a relatively larger risk profile value based on the risk factor, capacity, purchasing cost, transportation cost for each supplier and also demand from each manufacturer. Keywords : Shifting Order, Multiple Sourcing, Supply Side Risk, LinierProgramming

Propuesta metodológica de gestión de riesgos para el transporte de aguacate hass desde la línea de empaque hasta los puertos en el Valle del Cauca.

La gestión del riesgo en la cadena de suministro (GRCS) es un tema de gran interés académico y práctico, permite identificar, priorizar y mitigar los riesgos presentes en las actividades de la cadena que pueden generar un impacto significativo y afectar el buen desempeño organizacional. Este documento hace parte de un proyecto de investigación avalado por la Universidad del Valle, que tiene como objetivo establecer un plan de acción enfocado a la mitigación de los principales riesgos operacionales presentes en el transporte de aguacate Hass desde la línea de empaque hasta los puertos en el Valle del Cauca. De esta manera, se propone una metodología mixta, en una primera fase se establecen los riesgos del transporte mediante una revisión bibliográfica y se propone el uso de una matriz de probabilidad e impacto para la identificación de los riesgos operacionales presentes en el transporte dentro de la cadena, en una segunda fase el uso de AHP - Fuzzy para priorizar los principales riesgos, y posteriormente en la tercera fase se establecen las estrategias de mitigación de aquellos riesgos más relevantes que afectan los objetivos estratégicos o corporativos de la cadena de suministro de aguacate Hass.PregradoINGENIERO(A) EN INDUSTRIA

Developing a systematic methodology to build a systems dynamics model for assessment of non-technical risks in power plants

YesIn a dynamic business environment like the energy sector, power plants face several complex risks, including both technical and non-technical risks. These risks are not isolated, as their impact may affect a series of interrelated risks. Those risks may change with time, which in turn, makes the strategic decision-making process less effective. Understanding the dynamic behaviour of a complex system is very important to achieve a more sustainable overall performance of the power plants. Thus, it is important to further develop a systematic risk assessment methodology that could help to identify and analyse the interdependencies among risks and to understand the dynamics of these risks in complex systems. This paper develops a system dynamics (SD) methodology to support the development of risk assessment models. This paper highlights the environmental perspective. The first step to develop a SD model will be applied, while the final SD model will be discussed in another paper

Gestão de Riscos Logísticos em Cadeias de Suprimentos: Otimização via Metamodelo de Simulação.

Alguns tipos de riscos podem causar danos às cadeias de suprimentos, provocando rupturas nos fluxos de materiais e produtos acabados. Riscos logísticos se relacionam às falhas nos processos de transporte, armazenagem, produção e vendas. A gestão adequada desses riscos é fator crítico para a integração dos fluxos sob a responsabilidade da logística e operações, cujas atividades são frequentemente realizadas por provedores de serviços logísticos. Entretanto, observou-se a falta de procedimentos sistemáticos focados na gestão de riscos logísticos que melhor aproveitasse as vantagens da integração entre métodos de simulação e otimização. A pesquisa foi realizada em uma cadeia de suprimentos do segmento automotivo português, a partir de dados secundários disponíveis na literatura. Os problemas desse estudo foram: (a) quais os impactos dos riscos logísticos sobre o desempenho dessa cadeia? (b) sob a influência desses riscos, que ajustes no sistema logístico poderiam melhorar a resposta do arranjo aos impactos? Para solucionar tais questões, definiu-se como objetivo, mitigar os efeitos desses riscos a partir de um metamodelo de simulação para a otimização de parâmetros críticos. As atividades logísticas desempenhadas na cadeia de suprimentos foram escolhidas como objeto de estudo. Essa pesquisa foi classificada como aplicada, quantitativa e exploratória normativa, considerando, respectivamente, a sua natureza, a abordagem do problema e os objetivos. A simulação a eventos discretos, elaborada no ambiente Arena®, foi utilizada como método de pesquisa. A otimização Black Box, realizada através do software OptQuest®, foi empregada para projetar os parâmetros adequados para o sistema logístico. Um metamodelo de regressão baseado no método OLS foi desenvolvido a partir da projeção e implantação de experimentos, servindo para integrar as saídas do modelo de simulação às entradas do modelo de otimização. Inúmeras técnicas de verificação e validação foram empregadas para calibrar o modelo de otimização via simulação, tais como: implantação modular e análise de sensibilidade. Uma sistemática metodológica fundamentada na abordagem DMAIC foi elaborada para relacionar as etapas de gestão dos riscos logísticos e conduzir aos resultados dessa pesquisa, incluindo a identificação (Definir), avaliação (Mensuração), gestão (Melhoria e análise) e monitoramento (Controle) do risco logístico. Um evento de risco logístico foi inserido no modelo com o fim de reproduzir rupturas no fluxo físico de distribuição e permitir a avaliação dos seus impactos sobre o desempenho da cadeia. Os impactos foram medidos por meio do custo logístico total, do risco de ruptura e da taxa de nível de serviço. Estratégias de mitigação do risco logístico de transporte, como redundância e flexibilidade, foram testadas para minimizar simultaneamente custo e risco e maximizar a taxa de entrega. A solução sugerida pelo modelo multiobjetivo de otimização via simulação mostrou ser adequada e eficaz já que os ajustes no sistema logístico bloquearam as consequências da ruptura. A principal contribuição da pesquisa foi desenvolver procedimentos sistemáticos para melhorar a gestão de riscos logísticos no âmbito de cadeias de suprimentos a partir do uso combinado entre métodos de simulação e otimização

Knowledge-based Modelling of Additive Manufacturing for Sustainability Performance Analysis and Decision Making

Additiivista valmistusta on pidetty käyttökelpoisena monimutkaisissa geometrioissa, topologisesti optimoiduissa kappaleissa ja kappaleissa joita on muuten vaikea valmistaa perinteisillä valmistusprosesseilla. Eduista huolimatta, yksi additiivisen valmistuksen vallitsevista haasteista on ollut heikko kyky tuottaa toimivia osia kilpailukykyisillä tuotantomäärillä perinteisen valmistuksen kanssa. Mallintaminen ja simulointi ovat tehokkaita työkaluja, jotka voivat auttaa lyhentämään suunnittelun, rakentamisen ja testauksen sykliä mahdollistamalla erilaisten tuotesuunnitelmien ja prosessiskenaarioiden nopean analyysin. Perinteisten ja edistyneiden valmistusteknologioiden mahdollisuudet ja rajoitukset määrittelevät kuitenkin rajat uusille tuotekehityksille. Siksi on tärkeää, että suunnittelijoilla on käytettävissään menetelmät ja työkalut, joiden avulla he voivat mallintaa ja simuloida tuotteen suorituskykyä ja siihen liittyvän valmistusprosessin suorituskykyä, toimivien korkea arvoisten tuotteiden toteuttamiseksi. Motivaation tämän väitöstutkimuksen tekemiselle on, meneillään oleva kehitystyö uudenlaisen korkean lämpötilan suprajohtavan (high temperature superconducting (HTS)) magneettikokoonpanon kehittämisessä, joka toimii kryogeenisissä lämpötiloissa. Sen monimutkaisuus edellyttää monitieteisen asiantuntemuksen lähentymistä suunnittelun ja prototyyppien valmistuksen aikana. Tutkimus hyödyntää tietopohjaista mallinnusta valmistusprosessin analysoinnin ja päätöksenteon apuna HTS-magneettien mekaanisten komponenttien suunnittelussa. Tämän lisäksi, tutkimus etsii mahdollisuuksia additiivisen valmistuksen toteutettavuuteen HTS-magneettikokoonpanon tuotannossa. Kehitetty lähestymistapa käyttää fysikaalisiin kokeisiin perustuvaa tuote-prosessi-integroitua mallinnusta tuottamaan kvantitatiivista ja laadullista tietoa, joka määrittelee prosessi-rakenne-ominaisuus-suorituskyky-vuorovaikutuksia tietyille materiaali-prosessi-yhdistelmille. Tuloksina saadut vuorovaikutukset integroidaan kaaviopohjaiseen malliin, joka voi auttaa suunnittelutilan tutkimisessa ja täten auttaa varhaisessa suunnittelu- ja valmistuspäätöksenteossa. Tätä varten testikomponentit valmistetaan käyttämällä kahta metallin additiivista valmistus prosessia: lankakaarihitsaus additiivista valmistusta (wire arc additive manufacturing) ja selektiivistä lasersulatusta (selective laser melting). Rakenteellisissa sovelluksissa yleisesti käytetyistä metalliseoksista (ruostumaton teräs, pehmeä teräs, luja niukkaseosteinen teräs, alumiini ja kupariseokset) testataan niiden mekaaniset, lämpö- ja sähköiset ominaisuudet. Lisäksi tehdään metalliseosten mikrorakenteen karakterisointi, jotta voidaan ymmärtää paremmin valmistusprosessin parametrien vaikutusta materiaalin ominaisuuksiin. Integroitu mallinnustapa yhdistää kerätyn kokeellisen tiedon, olemassa olevat analyyttiset ja empiiriset vuorovaikutus suhteet, sekä muut tietopohjaiset mallit (esim. elementtimallit, koneoppimismallit) päätöksenteon tukijärjestelmän muodossa, joka mahdollistaa optimaalisen materiaalin, valmistustekniikan, prosessiparametrien ja muitten ohjausmuuttujien valinnan, lopullisen 3d-tulosteun komponentin halutun rakenteen, ominaisuuksien ja suorituskyvyn saavuttamiseksi. Valmistuspäätöksenteko tapahtuu todennäköisyysmallin, eli Bayesin verkkomallin toteuttamisen kautta, joka on vankka, modulaarinen ja sovellettavissa muihin valmistusjärjestelmiin ja tuotesuunnitelmiin. Väitöstyössä esitetyn mallin kyky parantaa additiivisien valmistusprosessien suorituskykyä ja laatua, täten edistää kestävän tuotannon tavoitteita.Additive manufacturing (AM) has been considered viable for complex geometries, topology optimized parts, and parts that are otherwise difficult to produce using conventional manufacturing processes. Despite the advantages, one of the prevalent challenges in AM has been the poor capability of producing functional parts at production volumes that are competitive with traditional manufacturing. Modelling and simulation are powerful tools that can help shorten the design-build-test cycle by enabling rapid analysis of various product designs and process scenarios. Nevertheless, the capabilities and limitations of traditional and advanced manufacturing technologies do define the bounds for new product development. Thus, it is important that the designers have access to methods and tools that enable them to model and simulate product performance and associated manufacturing process performance to realize functional high value products. The motivation for this dissertation research stems from ongoing development of a novel high temperature superconducting (HTS) magnet assembly, which operates in cryogenic environment. Its complexity requires the convergence of multidisciplinary expertise during design and prototyping. The research applies knowledge-based modelling to aid manufacturing process analysis and decision making in the design of mechanical components of the HTS magnet. Further, it explores the feasibility of using AM in the production of the HTS magnet assembly. The developed approach uses product-process integrated modelling based on physical experiments to generate quantitative and qualitative information that define process-structure-property-performance interactions for given material-process combinations. The resulting interactions are then integrated into a graph-based model that can aid in design space exploration to assist early design and manufacturing decision-making. To do so, test components are fabricated using two metal AM processes: wire and arc additive manufacturing and selective laser melting. Metal alloys (stainless steel, mild steel, high-strength low-alloyed steel, aluminium, and copper alloys) commonly used in structural applications are tested for their mechanical-, thermal-, and electrical properties. In addition, microstructural characterization of the alloys is performed to further understand the impact of manufacturing process parameters on material properties. The integrated modelling approach combines the collected experimental data, existing analytical and empirical relationships, and other data-driven models (e.g., finite element models, machine learning models) in the form of a decision support system that enables optimal selection of material, manufacturing technology, process parameters, and other control variables for attaining desired structure, property, and performance characteristics of the final printed component. The manufacturing decision making is performed through implementation of a probabilistic model i.e., a Bayesian network model, which is robust, modular, and can be adapted for other manufacturing systems and product designs. The ability of the model to improve throughput and quality of additive manufacturing processes will boost sustainable manufacturing goals

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