Combination of statistical process control (SPC) methods and classification strategies for situation assessment of batch processes

Postprint (published version

A knowledge-based distributed system for supervision and control of wastewater treatment processes

This paper presents the hardware architecture and the software development of a real-time knowledge-based distributed control system for the supervision of a wastewater treatment pilot plant with biological removal of organic matter and nitrogen. A continuous monitoring of plant and controls data is used by an expert system developed in G2, a development environment based on object-oriented paradigm. A set of rules and procedures to help fault detection, plant maintenance, and nitrification - denitrification cycle operation was implemented and validated at pilot scale. The hardware architecture includes different supervision levels, including two autonomous process computers (plant control and analysers control).Fundação Calouste Gulbenkian (FCG) - postdoctoral research grant..Generalitat de Catalunya. Consell Interdepartamental de Recerca i Innovació Tecnològica (CIRIT) - predoctoral fellowship

Towards an online mitigation strategy for N2O emissions through principal components analysis and clustering techniques

Emission of N2O represents an increasing concern in wastewater treatment, in particular for its large contribution to the plant's carbon footprint (CFP). In view of the potential introduction of more stringent regulations regarding wastewater treatment plants' CFP, there is a growing need for advanced monitoring with online implementation of mitigation strategies for N2O emissions. Mechanistic kinetic modelling in full-scale applications, are often represented by a very detailed representation of the biological mechanisms resulting in an elevated uncertainty on the many parameters used while limited by a poor representation of hydrodynamics. This is particularly true for current N2O kinetic models. In this paper, a possible full-scale implementation of a data mining approach linking plant-specific dynamics to N2O production is proposed. A data mining approach was tested on full-scale data along with different clustering techniques to identify process criticalities. The algorithm was designed to provide an applicable solution for full-scale plants' control logics aimed at online N2O emission mitigation. Results show the ability of the algorithm to isolate specific N2O emission pathways, and highlight possible solutions towards emission control

Fault detection and monitoring system using enhanced principal component analysis for the application in wastewater treatment plant

Fault detection and monitoring is essentially important in wastewater treatment to ensure that safety, environmental regulations compliance, maintenance and operation of the Wastewater Treatment Plant (WWTP) are under control. Many researchers have developed methods in fault detection and monitoring such as fuzzy logic, parameter estimation, neural network and Principal Component Analysis (PCA). In studies involving data and signal model approach, PCA is the most appropriate method used in this work. Besides when using PCA, the dimensionality of the data, noise and redundancy can be reduce. However, PCA is only suitable for data with mean constant or steady state data. The use of PCA can also increase false alarm and produce false fault in a plant such as WWTP. Modifications of PCA need to be done to overcome the problems and hence, enhanced methods of PCA are proposed in this work. The enhanced methods are Multiscale PCA (MSPCA) and Recursive PCA (RPCA), which are appropriate for offline monitoring test and online monitoring test, respectively. To see the effectiveness of the methods, they were applied into the european Co-operation in the field of Scientific and Technical Research (COST) simulation benchmark WWTP. The results from the simulation plant were then applied in a real WWTP, IWK Bunus Regional Sewage Treatment Plant (RSTP). The data of WWTP involved are Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD) and Nitrate (SNO). In analysis for both plants, faults were detected when the confidence limit is over 95% and confidence limits in the range of 90-95% were considered for alarm region in the data, using Hotelling's T2 and residual. Finally, simulation results of the proposed methods were compared and it was found that the enhanced methods of PCA (MSPCA and RPCA) were able to reduce false alarm and false fault in the analysis of fault detection by 70% for steady state influence and dynamic influence and hence provides more accurate results in detecting faults in the process data

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

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

Drivers of Microbial Risk for Direct Potable Reuse and de Facto Reuse Treatment Schemes: The Impacts of Source Water Quality and Blending.

Although reclaimed water for potable applications has many potential benefits, it poses concerns for chemical and microbial risks to consumers. We present a quantitative microbial risk assessment (QMRA) Monte Carlo framework to compare a de facto water reuse scenario (treated wastewater-impacted surface water) with four hypothetical Direct Potable Reuse (DPR) scenarios for Norovirus, Cryptosporidium, and Salmonella. Consumer microbial risks of surface source water quality (impacted by 0-100% treated wastewater effluent) were assessed. Additionally, we assessed risks for different blending ratios (0-100% surface water blended into advanced-treated DPR water) when source surface water consisted of 50% wastewater effluent. De facto reuse risks exceeded the yearly 10-4 infections risk benchmark while all modeled DPR risks were significantly lower. Contamination with 1% or more wastewater effluent in the source water, and blending 1% or more wastewater-impacted surface water into the advanced-treated DPR water drove the risk closer to the 10-4 benchmark. We demonstrate that de facto reuse by itself, or as an input into DPR, drives microbial risks more so than the advanced-treated DPR water. When applied using location-specific inputs, this framework can contribute to project design and public awareness campaigns to build legitimacy for DPR