Інтелектуальна CBR система для автоматизації процесу пошуку ефективних методів очищення викидних газів

В наш час більшість прикладних комп'ютерних засобів впроваджує методи та засоби штучного інтелекту. Одним з таких методів є так званий висновок заснований на прецедентах – Case based reasoning (CBR). Головна ідея CBR полягає у виборі рішення на основі досвіду. Тобто, розглянувши подібні випадки в минулому, можна класифікувати новий випадок. Саме цей підхід запропоновано авторами для використання в інтелектуальній системі вибору методів очищення викидних газів від оксидів азоту та сірки.In this study, the objective is to develop an intelligent system for making decisions on the choice of methods for cleaning exhaust gases from sulfur and nitrogen oxides using the Case-Based Reasoning- (CBR). The task of automating the selection of effective methods for cleaning waste gases is urgent and meets the paradigm of sustainable development. A database on methods for cleaning exhaust gases from nitrogen and sulfur oxides was created. The potential use of intelligent inference on precedents from the database to select the most appropriate cleaning method for new emission stream data is considered. The work of the CBR method is represented as a life cycle, which has four main stages: Retrieving, Reusing, Revising and Retaining. The following characteristics of precedents were considered: degree of purification, initial concentration, temperature, presence of impurities, obtained product, material consumption, and energy consumption. All of these characteristics (in CBR attributes), except for the fourth and fifth, are given by numerical values with respective units of measurement and can be easily normalized. The presence of impurities and the product are categorical attributes with a certain set of values (classes). One of the main problems in CBR was solved: the problem of choosing the type of indexes. A set of all input characteristics of the precedent as indices is suggested to be used for the proposed decision support system (DSS) for methods of cleaning gas emissions. The first two phases of the CBR lifecycle use the k-nearest neighbor method to Retrieving and Reusing. The Euclidean metric is used to estimate the distances between precedents in the developed system. During the third and fourth phases of CBR, the intervention of the decision maker is provided. The process finishes with the adoption of the found solution and the possible storage of this solution in the base of use cases. An intelligent decision-making system has been developed for the selection of methods for cleaning exhaust gases from sulfur and nitrogen oxides based on the method of inference by precedents (CBR), which has been done for the first time for such tasks of chemical technology