Application of adaptive neuro-fuzzy inference system for prediction of dissolved oxygen concentration in the gold cyanide leaching process

An adaptive neuro-fuzzy inference system (ANFIS) model has been developed for the prediction of the dissolved oxygen concentration (DOC) as a function of the solution temperature (0-40oC), salinity based on conductivity (0-59000 µS/cm), and atmospheric pressure (600-795 mmHg). The data set was randomly divided into two parts, training and testing sets. 80% of the data points (80% = 11556 datasets) were utilized for training the model and the remainder data points (20% =2889 datasets) were utilized for its testing. Several indices of performance such as root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of correlation (R) were used for checking the accuracy of data modeling. ANFIS models for the prediction of DOC were constructed with various types of membership functions (MFs). The model with the generalized bell MF had the best performance among all of the given models. The results indicate that ANFIS is a powerful tool for the accurate prediction of DOC in the gold cyanidation tanks

A NOVEL APPLICATION OF CHAOTIC PROPERTIES IN WATER TREATMENT PLANT

This paper aims at presenting a new optimization proposal to enhance the flocculation process in Water Treatment (WT) plant using a better flash mixing, located at KELAVERAPALLY, in Krishnagiri district, Tamil Nadu, India. Further, Sludge removal is done efficiently which decreases the water wastage as well as improvement in output water quality. Though WT plants are already equipped with systematic and sequential physicochemical processes, still they need to be optimized to obtain a better treated drinking water to maintain the quality standards as prescribed by World Health Organization. Chaotic behavior in chemical systems has been used to optimize the performance of WT plant. Measurement systems implemented in WT plant yield several chaotic based measurement parameters which are used to control the system operations to maintain the target water quality.  This intelligible data extraction through the proposed measurement  systems in a short span of time improves the plant performance without adding any costly systems except few changes in the existing plant setup.  Chaotic behavior is ensured through Lyapunov Exponents and Kolmogorov-Sinai Entropies. Both, water quality improvement and water wastage reduction is achieved simultaneously in the proposed work when a dosage prediction is done using Feed Forward Neural Networks. The treatment plant investigated has a maximum capacity of 14 MLD (Million litres per day) using two parallel streams with 7 MLD eac

Forbedring av koagulant-doseringskontroll i renseprosesser for vann og avløp

Chemical coagulation is one of the most important treatment processes in wastewater treatment and drinking water treatment. Defining the optimal coagulant dosage is a vital operation that decides the treatment efficiency and economy of the coagulation process. Chemical coagulation is a well-defined process where the optimal coagulant dosage is dependent on the influent quality, expressed by particle concentration, pH, temperature, colour or phosphate, alkalinity, etc. However, no conceptual model has been developed due to the complexity of this process and the research on coagulant dosage control has continued for decades (Ratnaweera and Fettig, 2015). Among all the avenues of research, the model predictive control based on online measurements is the most promising concept for coagulant dosage control. It presents various methods of model calibration and well-defined testing procedures. A Feed-Forward (FF) model based concept of a multi-parameter dosing control system for wastewater was originally proposed by Ratnaweera et al. (1994) and then improved upon by Lu (2003) and Rathnaweera (2010). According to previous results of full-scale tests, the multi-parameter dosing control system has proven to provide acceptable effluent quality and improved economy on most occasions in several wastewater treatment plants.Kjemisk felling er en av de viktigste enhetsprosessene i både avløps- og drikkevannsbehandling. Identifisering av optimal koagulantdose er sentralt i driften av koaguleringsprosessen, og avgjørende for både rensegraden og driftsøkonomien i prosessen. Kjemisk felling er en veldefinert prosess der den optimale koagulantdosen avhenger av kvaliteten på innkommende vann, gitt ved partikkelkonsentrasjon, pH, temperatur, farge eller fosfatinnhold, alkalinitet osv. Det finnes imidlertid ingen universielle konseptuell modell for å bestemme optimal dose ettersom prosessen er svært kompleks. Dette har ført til årtier med forskning på regulering av koagulantdosen (Ratnaweera og Fettig, 2015). Av de ulike forskningsretningene har prediktiv regulering basert på online målinger vist seg svært populært, og inkluderer forskjellige metoder for modellkalibrering og definerte testprosedyrer. Et konsept bestående av multi-parameter doseringsregulering for avløpsrensing ble opprinnelig foreslått av Ratnaweera et al. (1994) og forbedret av Lu (2003) og Rathnaweera (2010). Tidligere fullskala tester har vist at systemet for multi-parameter doseringsregulering gir akseptabel kvalitet på behandlet vann og forbedret driftsøkonomi i et antall avløpsbehandlingsanlegg.DOSCON A

Développement d'outils d'aide à l'opération du système de coagulation-floculation-décantation de l'usine de traitement des eaux de Sainte-Foy

Le procédé de coagulation constitue la première étape de traitement de la chaîne conventionnelle de production d’eau potable. La détermination du dosage optimal de coagulant à appliquer est relativement complexe puisqu’elle requiert l’atteinte simultanée de plusieurs objectifs. Il est donc pertinent de développer des outils d’aide à la décision pour assister les opérateurs dans le choix de la dose de coagulant. L’objectif de l’étude était de fournir des outils aux opérateurs de l’usine de traitement des eaux (UTE) de Sainte-Foy pour les aider dans le choix du dosage de sulfate d’aluminium (alun). Dans le cadre de ce projet, quatre outils ont été ainsi développés : un modèle de prédiction du dosage d’alun à appliquer, deux modèles de prédiction de la concentration en carbone organique dissous (COD) à l’eau décantée et un capteur virtuel qui permet de prédire la concentration en COD aux eaux brute et décantée. Dans tous les cas, il s’agit de modèles neuronaux. Le premier modèle permet de prédire le dosage d’alun à appliquer en reproduisant la bonne opération antérieure effectuée à l’usine en termes de réduction de la turbidité. Les variables d’entrée du modèle sont le mois, la conductivité, la température, la turbidité et le pH à l’eau brute. L’ajustement du modèle a été effectué à partir de données d’opérations récoltées aux 5 minutes pendant 4 années (378 535 séries de données). Les dosages prédits diffèrent en moyenne de 5,9% de ceux réellement appliqués. Le second modèle permet de prédire la concentration en COD à l’eau décantée à partir de l’absorbance ultraviolet (UV) à 254 nm et du COD à l’eau brute, du pH de coagulation et de la dose d’alun appliquée. Les performances du modèle 2 ont été comparées à celles obtenues à partir de deux autres modèles empiriques provenant de la littérature et permettant de prédire la concentration en COD après coagulation. Le modèle neuronal 2 a de meilleures performances de prédiction que ces deux autres modèles empiriques. Les concentrations en COD prédites par le modèle 2 diffèrent en moyenne de 9,6% de celles réelles. Le troisième modèle prédit la concentration en COD aux eaux brute et décantée à partir de l’absorbance UV, de la température, de la turbidité et du pH. Il agit à titre de capteur virtuel de COD et permet de rendre compte de l’efficacité de l’enlèvement de la matière organique naturelle par les étapes de coagulation, floculation et décantation. Les concentrations en COD prédites par le modèle 3 diffèrent en moyenne de 13,2% de celles réelles. Enfin, le quatrième modèle permet de prédire la concentration en COD à l’eau décantée à partir de l’absorbance UV (254 nm) à l’eau brute plutôt que du COD. Les concentrations prédites par ce dernier diffèrent en moyenne de 10,9% de celles réelles. La base de données utilisée pour l’ajustement des modèles 2, 3 et 4 comprend une année de suivi de COD et d’absorbance UV (eaux brute et décantée) à raison de 2 mesures par jour et les données d’opération récoltées en continu pour la même période. Les performances des quatre modèles sont présentées et discutées en fonction de leur implantation possible à l’usine et des améliorations pouvant leur être apportées. De tous les modèles développés, le seul qui pourrait être implanté à court terme est le modèle 1. En effet, les modèles 2, 3 et 4 sont préliminaires et devraient être mis à jour à partir de bases de données plus grandes comprenant davantage de périodes de variations et rendre de meilleures performances avant de pouvoir être implantés en usine. Les modèles développés pourraient être intégrés afin de permettre aux opérateurs de choisir le dosage de coagulant à appliquer qui permettrait de faire un compromis entre les différents objectifs du procédé de coagulation. Cela pourrait améliorer encore davantage la qualité de l’eau produite.The coagulation process is the first step of the conventional drinking water treatment chain. It is an important treatment step since it affects the efficiency of the subsequent treatment steps namely flocculation, settling, filtration and disinfection. It is relevant to develop decision aid tools to assist operators in the choice of the coagulant dose. This project aims at developing such tools. More specifically, the objective of the study was to provide tools for the operators of the Sainte-Foy water treatment plant to help them in choosing the appropriate aluminum sulphate dose (alum). As part of this project, three tools were developed: a model for the prediction of the coagulant dose, two models for the prediction of dissolved organic carbon (DOC) concentration of settled water and a virtual sensor which allows predicting DOC concentration of raw and settled waters. All models are neural network models. The first model allows the prediction of the alum dosage by mimicking the good previous operation performed at the plant in terms of turbidity reduction. The input variables of the model are the month, the conductivity, temperature, turbidity and pH of raw water. The model was developed from operation data collected every 5 minutes during 4 years (378 535 data sets). Dosages predicted vary by an average of 5,9% of those actually applied. The second model allows the prediction of the DOC of the settled water. The input variables are the UV absorbance and DOC of raw water, pH of coagulation and alum dosage applied. Performances of the second model are compared with those obtained from two others empirical models (from the literature) that allow the prediction of the DOC of the settled water. Compared to these models, the second neural model gives better prediction performance. DOC concentrations predicted by the second model vary by an average of 9,6% of those actually measured. The third model allows the prediction of the DOC of raw and settled water. The input variables are the UV absorbance, temperature, turbidity and pH. The model acts as a virtual sensor of DOC concentration and allows the evaluation of the removal efficiency of natural organic matter by the coagulation, flocculation and settling steps. DOC concentrations predicted by the third model vary by an average of 13,2% of those actually measured. Finally, the fourth model allows the prediction of the DOC of settled water from UV absorbance of raw water instead of DOC. Concentrations predicted by that model vary by an average of 10,7% of those actually measured. The database for the adjustment of the second, third, and fourth models includes one year of DOC and UV absorbance monitoring at raw and settled water performed twice daily and operation data continuously collected. The models performances are presented and discussed according to their implementation and use in the treatment plant. A way to improve developed models is also described. Actually, only the first model could be implemented on a short term basis. Models 2, 3 and 4 are actually preliminary models that would need to be updated with larger databases including more variation periods before implementation. Developed models could be integrated to allow the operators to choose the alum dosage that can afford to make a compromise between the different objectives of the coagulation process. This could further improve the treated water quality

Pathways to Water Sector Decarbonization, Carbon Capture and Utilization

The water sector is in the middle of a paradigm shift from focusing on treatment and meeting discharge permit limits to integrated operation that also enables a circular water economy via water reuse, resource recovery, and system level planning and operation. While the sector has gone through different stages of such revolution, from improving energy efficiency to recovering renewable energy and resources, when it comes to the next step of achieving carbon neutrality or negative emission, it falls behind other infrastructure sectors such as energy and transportation. The water sector carries tremendous potential to decarbonize, from technological advancements, to operational optimization, to policy and behavioural changes. This book aims to fill an important gap for different stakeholders to gain knowledge and skills in this area and equip the water community to further decarbonize the industry and build a carbon-free society and economy. The book goes beyond technology overviews, rather it aims to provide a system level blueprint for decarbonization. It can be a reference book and textbook for graduate students, researchers, practitioners, consultants and policy makers, and it will provide practical guidance for stakeholders to analyse and implement decarbonization measures in their professions

Pathways to Water Sector Decarbonization, Carbon Capture and Utilization

The water sector is in the middle of a paradigm shift from focusing on treatment and meeting discharge permit limits to integrated operation that also enables a circular water economy via water reuse, resource recovery, and system level planning and operation. While the sector has gone through different stages of such revolution, from improving energy efficiency to recovering renewable energy and resources, when it comes to the next step of achieving carbon neutrality or negative emission, it falls behind other infrastructure sectors such as energy and transportation. The water sector carries tremendous potential to decarbonize, from technological advancements, to operational optimization, to policy and behavioural changes. This book aims to fill an important gap for different stakeholders to gain knowledge and skills in this area and equip the water community to further decarbonize the industry and build a carbon-free society and economy. The book goes beyond technology overviews, rather it aims to provide a system level blueprint for decarbonization. It can be a reference book and textbook for graduate students, researchers, practitioners, consultants and policy makers, and it will provide practical guidance for stakeholders to analyse and implement decarbonization measures in their professions

Pathways to Water Sector Decarbonization, Carbon Capture and Utilization

The water sector is in the middle of a paradigm shift from focusing on treatment and meeting discharge permit limits to integrated operation that also enables a circular water economy via water reuse, resource recovery, and system level planning and operation. While the sector has gone through different stages of such revolution, from improving energy efficiency to recovering renewable energy and resources, when it comes to the next step of achieving carbon neutrality or negative emission, it falls behind other infrastructure sectors such as energy and transportation. The water sector carries tremendous potential to decarbonize, from technological advancements, to operational optimization, to policy and behavioural changes. This book aims to fill an important gap for different stakeholders to gain knowledge and skills in this area and equip the water community to further decarbonize the industry and build a carbon-free society and economy. The book goes beyond technology overviews, rather it aims to provide a system level blueprint for decarbonization. It can be a reference book and textbook for graduate students, researchers, practitioners, consultants and policy makers, and it will provide practical guidance for stakeholders to analyse and implement decarbonization measures in their professions

Pathways to Water Sector Decarbonization, Carbon Capture and Utilization

The water sector is in the middle of a paradigm shift from focusing on treatment and meeting discharge permit limits to integrated operation that also enables a circular water economy via water reuse, resource recovery, and system level planning and operation. While the sector has gone through different stages of such revolution, from improving energy efficiency to recovering renewable energy and resources, when it comes to the next step of achieving carbon neutrality or negative emission, it falls behind other infrastructure sectors such as energy and transportation. The water sector carries tremendous potential to decarbonize, from technological advancements, to operational optimization, to policy and behavioural changes. This book aims to fill an important gap for different stakeholders to gain knowledge and skills in this area and equip the water community to further decarbonize the industry and build a carbon-free society and economy. The book goes beyond technology overviews, rather it aims to provide a system level blueprint for decarbonization. It can be a reference book and textbook for graduate students, researchers, practitioners, consultants and policy makers, and it will provide practical guidance for stakeholders to analyse and implement decarbonization measures in their professions