Application of different models for management of drinking water quality

Imati pitku vodu nameće obvezu i očuvanja i zaštitu od zagađenja preko uspostavljanja zaštitnih zona izvorišta. Ako se zagađenje, pored svih mjera ipak dogodi, potrebno je uvesti tehnike pripreme vode za piće kako bi voda zadovoljavala sve propisane uvjete o zdravstvenoj ispravnosti. U radu su prikazani uobičajeni načini pripreme vode za piće, kao i pregled dostupnih modela za simulaciju kvalitete pitke vode. Štoviše, ovi se modeli koriste za optimizaciju stanica za pripremu vode za piće. Posebno izvješće je dano za modele Otter i Stimela okruženja kao osnove za razvoj novih modela za pripremu pitke vode u okviru međunarodnih projekata. Prezentirani su neki pozitivni primjeri korištenja pojedinačnih procesa iz Stimela okruženja modeliranja u svijetu i kod nas.Access to safe drinking water imposes an obligation to preserve and protect it against pollution through the establishment of protection zones of the source. If pollution, despite of all the measures still occurs, it is necessary to introduce drinking water preparation techniques so that it meets all the prescribed health conditions. The paper presents conventional drinking water preparation methods along with the review of available models for simulating the drinking water quality. Moreover, these models are used for optimising drinking water production plants. A particular review was given for the Otter and Stimela environment models as a basis for developing new drinking water models within international projects. In this paper some positive examples of using individual processes from the Stimela environment are presented in the world and in our country

Improved Decision Support For Water Treatment Plant Operations Via Stochastic Water Quality, Multi-Objective Optimization, and Visual Analytics

Water treatment decision support systems (DSSs) enhance the quality and consistency of decision making in the production and distribution of drinking water. In this work, we review the state-of-the-art for water treatment DSSs and identify three critical research areas&mdash;stochastic water quality, multi-objective optimization, and visual analytics. We advance these research areas through the development of statistical methods, a novel optimization framework, and an interactive visualization software library. Stochastic water quality methods are useful for generating water quality scenarios to characterize source water uncertainty and analyze how variations in water quality impact treatment decisions. However, traditional time series approaches fail to accurately model these data given the short, multivariate nature of water quality records. Due to these data limitations, we developed a non-parametric approach, known as the modified k-Nearest Neighbor (k-NN) bootstrap resampling, which can generate water quality scenarios that capture the statistical properties of historical records. Using a simulation model, the performance of a treatment plant can be evaluated over an ensemble of water quality scenarios. By coupling this simulation with multi-objective optimization, an algorithm can search for alternative operating policies that perform well across these scenarios for multiple treatment objectives. To better understand the conflict between these objectives&mdash;such as disinfection goals, disinfection byproduct formation, and cost&mdash;and to improve treatment operations, we created a new optimization framework that integrates the modified k-NN described above with simulation-optimization techniques. Visual analytics are used to explore the large, high-dimensional datasets produced by treatment optimizations. These iterative, interactive visualizations facilitate decision making, and among visualization methods, parallel coordinates are well-suited for these datasets. However, current software and open source libraries have limited support for these plots. To improve the implementation of parallel coordinates visualizations and to increase access to visual analytics, we created Parasol&mdash;an open source, interactive visualization library to support the development of web applications for multi-objective decision making.</p

Optimisation of water treatment works using Monte-Carlo methods and genetic algorithms

Hand movements reveal the temporal characteristics of visual attention Optimisation of potable water treatment could result in substantial cost savings for water companies and their customers. To address this issue, computational modelling of water treatment works using static and dynamic models was examined alongside the application of optimisation techniques including genetic algorithms and operational zone identification. These methods were explored with the assistance of case study data from an operational works. It was found that dynamic models were more accurate than static models at predicting the water quality of an operational site but that the root mean square error of the models was within 5% of each other for key performance criteria. Using these models, a range of abstraction rates, for which a water treatment works was predicted to operate sufficiently, were identified, dependent on raw water temperature and total organic carbon concentration. Genetic algorithms were also applied to the water treatment works models to identify near optimal design and operating regimes. Static models were identified as being more suitable for whole works optimisation than dynamic models based on their relative accuracy, simplicity and computational demands