Design acceleration in chemical engineering

Nowadays, Chemical Engineering has to face a new industrial context with for example: the gradually falling of hydrocarbon reserves after 2020-2030, relocation, emerging of new domains of application (nano-micro technologies) which necessitate new solutions and knowledges… All this tendencies and demands accelerate the need of tool for design and innovation (technically, technologically). In this context, this paper presents a tool to accelerate innovative preliminary design. This model is based on the synergy between: TRIZ (Russian acronym for Theory of Inventive Problem Solving) and Case Based Reasoning (CBR). The proposed model offers a structure to solve problem, and also to store and make available past experiences in problems solving. A tool dedicated to chemical engineering problems, is created on this model and a simple example is treated to explain the possibilities of this tool

Case Based Reasoning for Chemical Engineering Design

With current industrial environment (competition, lower profit margin, reduced time to market, decreased product life cycle, environmental constraints, sustainable development, reactivity, innovation…), we must decrease the time for design of new products or processes. While the design activity is marked out by several steps, this article proposed a decision support tool for the preliminary design step. This tool is based on the Case Based Reasoning (CBR) method. This method has demonstrated its effectiveness in other domains (medical, architecture…) and more recently in chemical engineering. This method, coming from Artificial Intelligence, is based on the reusing of earlier experiences to solve new problems. The goal of this article is to show the utility of such method for unit operation (for example) pre-design but also to propose several evolutions for CBR through a domain as complex as the chemical engineering is (because of its interactions, non linearity, intensification problems…). During the pre-design step, some parameters like operating conditions are not precisely known but we have an interval of possible values, worse we only have a partial description of the problem.. To take into account this imprecision in the problem description, the CBR method is coupled with the fuzzy sets theory. After a mere presentation of the CBR method, a practical implementation is described with the choice and the pre-design of packing for separation columns

Case Based Reasoning and TRIZ : a coupling for Innovative conception in Chemical Engineering

With the evolutions of the surrounding world market, researchers and engineers have to propose technical innovations. Nevertheless, Chemical Engineering community demonstrates a small interest for innovation compared to other engineering fields. In this paper, an approach to accelerate inventive preliminary design for Chemical Engineering is presented. This approach uses Case Based Reasoning (CBR) method to model, to capture, to store and to make available the knowledge deployed during design. CBR is a very interesting method coming from Artificial Intelligence, for routine design. Indeed, in CBR the main assumption is that a new problem of design can be solved with the help of past successful ones. Consequently, the problem solving process is based on past successful solutions therefore the design is accelerated but creativity is limited and not stimulated. Our approach is an extension of the CBR method from routine design to inventive design. One of the main drawbacks of this method is that it is restricted in one particular domain of application. To propose inventive solution, the level of abstraction for problem resolution must be increased. For this reason CBR is coupled with the TRIZ theory (Russian acronym for Theory of solving inventive problem). TRIZ is a problem solving method that increases the ability to solve creative problems thanks to its capacity to give access to the best practices in all the technical domains. The proposed synergy between CBR and TRIZ combines the main advantages of CBR (ability to store and to reuse rapidly knowledge) and those of TRIZ (no trade off during resolution, inventive solutions). Based on this synergy, a tool is developed and a mere example is treated

Modeling and analysis of process failures using probabilistic functional model

Failure analysis is an important tool for effective safety management in the chemical process industry. This thesis applies a probabilistic approach to study two failure analysis techniques. The first technique focuses on fault detection and diagnosis (FDD), while the second is on vulnerability analysis of plant components. In formulating the FDD strategy, a class of functional model called multilevel flow modeling (MFM) was used. Since this model is not commonly used for chemical processes, it was tested on a crude distillation unit and validated using a simulation flowsheet implemented in Aspen HYSYS (Version 8.4) to demonstrate its suitability. Within the proposed FDD framework, probabilistic information was added by transforming the MFM model into its equivalent fault tree model to provide the ability to predict the likelihood of component’s failure. This model was then converted into its equivalent Bayesian network model using HUGIN 8.1 software to facilitate computations. Evaluations of the system on a heat exchanger pilot plant highlight the capability of the model in detecting process faults and identifying the associated root causes. The proposed technique also incorporated options for multi – state functional outcomes, in addition to the typical binary states offered by typical MFM model. The second tool proposed was a new methodology called basic event ranking approach (BERA), which measures the relative vulnerabilities of plant components and can be used to assist plant maintenance and upgrade planning. The framework was applied to a case study involving toxic prevention barriers in a typical process plant. The method was compared to some common importance index methodologies, and the results obtained ascertained the suitability of BERA to be used as a tool to facilitate risk based decisions in planning maintenance schedules in a process plant

Flexible knowledge representation and new similarity measure: Application on case based reasoning for waste treatment

In Case Based Reasoning the representation of a case and the similarity measures are two difficult steps in the conception of a system. Often, these steps are developed to resolve one kind of problem. However, in some of them such as recovery treatment processes generation, it is necessary for the system to be able to modify and adapt the representation of a case and the similarity measures with respect of the context and also the kind of solutions proposed. In this paper, authors introduce a new method to represent cases with a flexibility based on a structure in a connectionist model. This flexibility is needed due to the complexity of cases, the number of possible options and to ensure the durability of the system. In a second main contribution, authors introduce a method for the selection of source cases using abstraction, conceptualisation and inference mechanisms. Finally, authors test their system in a CBR developed on SWI-Prolog with different problems. The CBR is applied to find new recovery processes and try to estimate the new upgraded product generated

Sistemas baseados em conhecimento para projeto de plantas de cogeração a gás natural

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-graduaçao em Engenharia MecânicaCogeração é definida como a produção simultânea de energia eletromecânica e energia térmica útil a partir de uma mesma fonte energética, permitindo assim que a energia contida no combustível seja utilizada de modo mais eficiente e racional do que a geração independente de energia elétrica e calor. Conseqüentemente, tem um forte apelo tanto do ponto de vista de eficiência, devido principalmente aos custos de ombustíveis e impactos ambientais, como do ponto de vista de geração distribuída de energia, já que, dada a proximidade entre a planta e o local de consumo, dispensam-se subestações e linhas de transmissão de alta tensão. O projeto de uma planta de cogeração é um problema de síntese sujeito fundamentalmente a restrições termodinâmicas. Inclui a alocação e dimensionamento de componentes diversos, de modo a satisfazer as demandas de energia elétrica e de calor útil. Apesar de sua complexidade, é um problema que é resolvido de modo robusto por especialistas, o que sugere o uso de ferramentas de inteligência artificial (IA) para resolvê-lo computacionalmente, tais como Sistemas Especialistas (SE) e Raciocínio Baseado em Casos (RBC). No presente trabalho são desenvolvidos dois protótipos de sistemas computacionais inteligentes, baseados nas técnicas SE e RBC, respectivamente. Os protótipos são utilizados para apoio às fases de projeto conceitual e preliminar de plantas de cogeração, considerando cargas de energia elétrica, vapor saturado e água gelada como requisitos de projeto. Os protótipos apresentam características que não são encontradas em nenhum sistema computacional para esse domínio disponível até o momento, tais como explicação da solução (no protótipo SE) e aprendizado a partir da própria experiência (no protótipo RBC). A combinação das duas técnicas em um único protótipo é também discutida. Finalmente, o uso dos protótipos é demonstrado através da resolução de alguns casos selecionados, sendo que cada um representa um diferente conjunto de requisitos de projeto. Cogeneration is defined as the simultaneous production of power and useful thermal energy from the same energy source, so that the fuel energy is used in a more rational and efficient way when compared to the separated production of power and heat. Thus, it has a strong appeal from both an efficiency point of view (due mainly to the fuel costs and environmental impact) and a distributed generation point of view, for the proximity between the plant and the user makes substations and transmission lines superfluous. The design of a cogeneration plant is a synthesis problem subject to thermodynamic constraints. It includes allocation and sizing of several components, such that power and useful heat demands must be satisfied. Despite its complexity, it is a problem that is robustly solved by human experts, which suggests the use of artificial intelligence (AI) tools to solve it computationally. Well known AI tools are Expert Systems (ES) and Case-Based Reasoning (CBR). In this work, two intelligent computational prototypes are developed, based on ES and CBR techniques, respectively. The prototypes are used to support the conceptual and preliminary phases of the cogeneration plant design, considering power, saturated steam and chilled water as design requirements. The prototypes present characteristics # such as solution explanation (ES prototype) and learning from previous experiences (CBR prototype) # that are not known in any of the available computational systems in this engineering domain. The combination of both ES and CBR techniques in a single prototype is discussed as well. Finally, the prototype use is shown by solving some selected cases, each case representing a different set of design requirements