On the role of pre and post-processing in environmental data mining

The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Providing intelligent decision support systems with flexible data-intensive case-based reasoning

In this paper we present a flexible CBR shell for Data-Intensive Case-Based Reasoning Systems which is fully integrated in an Intelligent Data Analysis Tool entitled GESCONDA. The main subgoal of the developed tool is to create a CBR Shell where no fixed domain exists and where letting the expert/user creates (models) his/her own domain. From an abstract point of view, the definition of the CBR can be seen as a methodology composed by four phases and each phase offers different ways to be solved. Then, since the CBR shell is integrated in GESCONDA, it inherits all its functionalities which cover the whole knowledge discovery and data mining process and also, CBR can complement its phases with this functionality. As a result, GESCONDA becomes an intelligent decision support tool which encompasses a number of advantages including domain independence, incremental learning, platform independence and generality.Peer ReviewedPostprint (published version

Decision support systems (DSS) for wastewater treatment plants: a review of the state of the art

The use of decision support systems (DSS) allows integrating all the issues related with sustainable developmentin view of providing a useful support to solve multi-scenario problems. In this work an extensive review on theDSSs applied to wastewater treatment plants (WWTPs) is presented. The main aim of the work is to provide anupdated compendium on DSSs in view of supporting researchers and engineers on the selection of the mostsuitable method to address their management/operation/design problems. Results showed that DSSs weremostly used as a comprehensive tool that is capable of integrating several data and a multi-criteria perspective inorder to provide more reliable results. Only one energy-focused DSS was found in literature, while DSSs based onquality and operational issues are very often applied to site-specific conditions. Finally, it would be important toencourage the development of more user-friendly DSSs to increase general interest and usability.This work is part of a research project supported by grant of the Italian Ministry of Education, University and Research (MIUR) through the Research project of national interest PRIN2012 (D.M. 28 December 2012 n. 957/Ric – Prot. 2012PTZAMC) entitled “Energy consumption and Greenhouse Gas (GHG) emissions in the wastewater treatment plants: a decision support system for planning and management – http://ghgfromwwtp.unipa.it” in which the first author is the Principal Investigator. In addition, some coauthors acknowledge the partial support of the Industrial Doctorate Programme (2017-DI-006) and the Research Consolidated Groups/Centres Grant (2017 SGR 574) from the Catalan Agency of University and Research Grants Management (AGAUR), from Catalan Government.Peer ReviewedPostprint (author's final draft

Development of an intelligent dynamic modelling system for the diagnosis of wastewater treatment processes

In the 21st Century, water is already a limited and valuable resource, in particular the limited availability of fresh water sources. The projected increase in global population from 6 billion people in 2010 to 9 billion in 2050 will only increase the need for additional water sources to be identified and used. This situation is common in many countries and is frequently exacerbated by drought conditions. Water management planning requires both the efficient use of water sources and, increasingly, the re-use of domestic and industrial wastewaters. A large body of published research spanning several decades is available, and this research study looks specifically at ways of improving the operation of wastewater treatment processes.Process fault diagnosis is a major challenge for the chemical and process industries, and is also important for wastewater treatment processes. Significant economic and environmental losses can be attributed to inappropriate Abnormal Event Management (AEM) in a chemical/processing operation, and this has been the focus of many researchers. Many researchers are now focusing on the application of several fault diagnosis techniques simultaneously in order to improve and overcome the limitations experienced by the individual techniques. This approach requires resolution of the conflicts ascribed to the individual methods, and incurs additional costs and resources when employing more than one technique. The research study presented in this thesis details a new method of using the available techniques. The proposal is to use different techniques in different roles within the diagnostic approach based upon their inherent individual strengths. The techniques that are excellent for the detection of a fault should be employed in the fault detection, and those best applied to diagnosis are used in the diagnosis section of a diagnostic system.Two different techniques are used here, namely a mathematical model and data mining are used for detection and diagnosis respectively. A mathematical model is used which is based upon the principal of analytical redundancy in order to establish the presence of a fault in a process (the fault detection), and data mining is used to produce production rules derived from the historical data for the diagnosis. A dataset from an industrial wastewater treatment facility is used in this study.A diagnostic algorithm has been developed that employs the techniques identified above. An application in Java was constructed which allows the algorithm to be applied, eventually producing an intelligent modelling agent. Thus the focus of this research work was to develop an intelligent dynamic modelling system (using components such as mathematical model, data mining, diagnostic algorithm, and the dataset) for simulation of, and diagnosis of faults in, a wastewater treatment process where different techniques will be assigned different roles in the diagnostic system.Results presented in Chapter 5 (section 5.5) show that the application of this combined technique yields better results for detection and diagnosis of faults in a process. Furthermore, the dynamic update of the set value for any process variable (presented in Chapter 5, section 5.2.1) makes possible the detection of any process disturbance for the algorithm, thereby mitigating the issue of false alarms. The successful embedding of both a detection and a diagnostic technique in a single algorithm is a key achievement of this work, thus reducing the time taken to detect and diagnose a fault. In addition, the implementation of the algorithm in the purposebuilt software platform proved its practical application and potential to be used in the chemical and processing industries

The fourth-revolution in the water sector encounters the digital revolution

The so-called fourth revolution in the water sector will encounter the Big data and Artificial Intelligence (AI) revolution. The current data surplus stemming from all types of devices together with the relentless increase in computer capacity is revolutionizing almost all existing sectors, and the water sector will not be an exception. Combining the power of Big data analytics (including AI) with existing and future urban water infrastructure represents a significant untapped opportunity for the operation, maintenance, and rehabilitation of urban water infrastructure to achieve economic and environmental sustainability. However, such progress may catalyze socio-economic changes and cross sector boundaries (e.g., water service, health, business) as the appearance of new needs and business models will influence the job market. Such progress will impact the academic sector as new forms of research based on large amounts of data will be possible, and new research needs will be requested by the technology industrial sector. Research and development enabling new technological approaches and more effective management strategies are needed to ensure that the emerging framework for the water sector will meet future societal needs. The feature further elucidates the complexities and possibilities associated with such collaborations.Manel Garrido-Baserba and Diego Rosso acknowledge the United States Department of Energy (CERC-WET US Project 525 2.5). Lluís Corominas acknowledges the Ministry of Economy and competitiveness for the Ramon and Cajal grant (RYC2013-465 14595) and the following I3. We thank Generalitat de Catalunya through Consolidated Research Group 2017 SGR 1318. ICRA researchers acknowledge funding from the CERCA program.Peer ReviewedPostprint (author's final draft

Knowledge discovery and data mining to understand and optimise the environmental behavior of wastewater treatment processes

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonDirect nitrous oxide (N2O) emissions during the biological nitrogen removal (BNR) processes can significantly increase the carbon footprint of wastewater treatment plant (WWTP) operations. However, quantifying the emissions and understanding the long-term behaviour of N2O fluxes in WWTPs remains challenging and costly. The aim of the current research is to combine wastewater domain knowledge with data-mining techniques to explain the long-term N2O emissions’ behaviour in full-scale biological reactors. A review of the recent full-scale N2O monitoring campaigns is conducted resulting in the development of an emission factor (EF) database with information on configurations, control strategies and operational conditions. The analysis focused on mechanistic model development, molecular biology methods and on the current data management and analysis practices (i.e. visualization techniques, statistical analysis). Sensor and laboratory data acquired from the N2O monitoring campaigns of mainstream and sidestream wastewater processes were used to develop, test and validate a methodological framework for knowledge discovery in wastewater databases. Abnormal events detection, structural changepoint detection, clustering, classification and regression algorithms are used in order to i) translate data into actionable information, ii) link N2O emissions ranges with specific operational conditions, iii) identify and isolate re-occurring system disturbances that affect performance, iv) predict the range of N2O emissions based on operational and environmental conditions and v) provide feedback to monitoring campaigns for the minimisation of sampling requirements. The analysis showed that the relationship of N2O emissions with the operational variables fluctuates in long-term monitoring campaigns; this should be taken into consideration for the development of mitigation measures and during the investigation of triggering operational conditions. Additionally, findings indicate that structural changepoints of operational variables monitored online can be used to detect changes in the behaviour and range of N2O emissions. Finally, data-driven models can reliably estimate N2O behaviour in wastewater processes under given operational conditions. However, fluctuation of dependencies, system disturbances and process-specific characteristics should be taken into consideration

A Competency Mapping for Educational Institution: Expert System Approach

This paper presents the development of expert system to assist in the operation of competence management in educational institution. The knowledge based consists of a rule-based expert system for the competence management and subsequent performance assessment. It is generally recognized that an expert system can cope with many of the common problems relative with the operation and control of the competence management process. In this work an expert system is developed which emphasize on various steps involved in the competence management process. The knowledge acquisition to develop this expert system involved an exhaustive literature review on competence management operation and interviews with experienced deans and the competence managers. The development tool for this system is an expert system shell

Machine learning based anomaly detection for industry 4.0 systems.

223 p.This thesis studies anomaly detection in industrial systems using technologies from the Fourth Industrial Revolution (4IR), such as the Internet of Things, Artificial Intelligence, 3D Printing, and Augmented Reality. The goal is to provide tools that can be used in real-world scenarios to detect system anomalies, intending to improve production and maintenance processes. The thesis investigates the applicability and implementation of 4IR technology architectures, AI-driven machine learning systems, and advanced visualization tools to support decision-making based on the detection of anomalies. The work covers a range of topics, including the conception of a 4IR system based on a generic architecture, the design of a data acquisition system for analysis and modelling, the creation of ensemble supervised and semi-supervised models for anomaly detection, the detection of anomalies through frequency analysis, and the visualization of associated data using Visual Analytics. The results show that the proposed methodology for integrating anomaly detection systems in new or existing industries is valid and that combining 4IR architectures, ensemble machine learning models, and Visual Analytics tools significantly enhances theanomaly detection processes for industrial systems. Furthermore, the thesis presents a guiding framework for data engineers and end-users