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

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

Innovation and Knowledge Management : using the combined approach TRIZ-CBR in Process System Engineering

In this article, a TRIZ based model is proposed to support the innovation and knowledge capitalization process. This model offers a knowledge base structure, which contains several heuristics to solve problems, synthesized from a large range of domains and industries and, also, the capacity to capture, store and make available the experiences produced while solving problems

The TRIZ-CBR synergy: A knowledge based innovation process

Today innovation is recognised as the main driving force in the market. This complex process involves several intangible dimensions, such as creativity, knowledge and social interactions among others. Creativity is the starting point of the process, and knowledge is the force that transforms and materialises creativity in new products, services and processes. In this paper a synergy that aims to assists the innovation process is presented. The synergy combines several concepts and tools of the theory of inventive problem solving (TRIZ) and the case-based reasoning (CBR) process. The main objective of this synergy is to support creative engineering design and problem solving. This synergy is based on the strong link between knowledge and action. In this link, TRIZ offers several concepts and tools to facilitate concept creation and to solve problems, and the CBR process offers a framework capable of storing and reusing knowledge with the aim of accelerating the innovation process

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Improving Knowledge Retrieval in Digital Libraries Applying Intelligent Techniques

Nowadays an enormous quantity of heterogeneous and distributed information is stored in the digital University. Exploring online collections to find knowledge relevant to a user’s interests is a challenging work. The artificial intelligence and Semantic Web provide a common framework that allows knowledge to be shared and reused in an efficient way. In this work we propose a comprehensive approach for discovering E-learning objects in large digital collections based on analysis of recorded semantic metadata in those objects and the application of expert system technologies. We have used Case Based-Reasoning methodology to develop a prototype for supporting efficient retrieval knowledge from online repositories. We suggest a conceptual architecture for a semantic search engine. OntoUS is a collaborative effort that proposes a new form of interaction between users and digital libraries, where the latter are adapted to users and their surroundings

Constructing Ontology-Based Cancer Treatment Decision Support System with Case-Based Reasoning

Decision support is a probabilistic and quantitative method designed for modeling problems in situations with ambiguity. Computer technology can be employed to provide clinical decision support and treatment recommendations. The problem of natural language applications is that they lack formality and the interpretation is not consistent. Conversely, ontologies can capture the intended meaning and specify modeling primitives. Disease Ontology (DO) that pertains to cancer's clinical stages and their corresponding information components is utilized to improve the reasoning ability of a decision support system (DSS). The proposed DSS uses Case-Based Reasoning (CBR) to consider disease manifestations and provides physicians with treatment solutions from similar previous cases for reference. The proposed DSS supports natural language processing (NLP) queries. The DSS obtained 84.63% accuracy in disease classification with the help of the ontology

Semantic-driven matchmaking of web services using case-based reasoning

With the rapid proliferation of Web services as the medium of choice to securely publish application services beyond the firewall, the importance of accurate, yet flexible matchmaking of similar services gains importance both for the human user and for dynamic composition engines. In this paper, we present a novel approach that utilizes the case based reasoning methodology for modelling dynamic Web service discovery and matchmaking. Our framework considers Web services execution experiences in the decision making process and is highly adaptable to the service requester constraints. The framework also utilises OWL semantic descriptions extensively for implementing both the components of the CBR engine and the matchmaking profile of the Web services