On-Line Construction and Rule Base Simplification by Replacement in Fuzzy Systems Applied to a Wastewater Treatment Plant

Evolving Takagi-Sugeno (eTS) fuzzy models are used to build a computational model for the WasteWater Treatment Plant (WWTP) in a paper mill. The fuzzy rule base is constructed on-line from data using a recursive fuzzy clustering algorithm that develops the model structure and parameters. In order to avoid some redundancy in the fuzzy rule base mechanisms for merging membership functions and simplifying fuzzy rules are introduced. The rule base simplification is done by replacement allowing the preservation of the rule (cluster) centres as data points belonging to the original data set. Results for the WWTP show that it is possible to build less complex models and preserve a good balance between accuracy and transparency. Copyright © 2005 IFA

Control of the Coagulation Process in a Paper-mill Wastewater Treatment Process Using a Fuzzy Neural Network

In this paper, an integrated neural-fuzzy process controller was developed to study the coagulation of wastewater treatment in a paper mill. In order to improve the fuzzy neural network performance, the self-learning ability embedded in the fuzzy neural network model was emphasized for improving the rule extraction performance. It proves the fuzzy neural network more effective in modeling the coagulation performance than artificial neural networks (ANN). For comparing between the fuzzy neural controller and PID controller, a coagulation unit in a paper mill wastewater treatment process (PMWTP) was chosen to support the derivation of a fuzzy control rule base. It is shown that, using the fuzzy neural controller, in terms of cost effectiveness, enables us to save almost 25 % of the operating costs during the time when the controller can be applied

ESTIMATION OF GREENHOUSE GAS AND ODOUR EMISSIONS FROM COLD REGION MUNICIPAL BIOLOGICAL NUTRIENT REMOVAL WASTEWATER TREATMENT PROCESSES

Rising human populations and ever-increasing demand for potable water result in increased municipal wastewater production. The collection, treatment, and management of municipal wastewaters include energy-intensive processes leading to the generation and emission of greenhouse, potentially toxic, and odorous gases. The main goal of this thesis was to advance knowledge of greenhouse gas (including carbon dioxide, CO2; methane, CH4; and nitrous oxide, N2O) and smelly compound (including ammonia, NH3; and hydrogen sulphide, H2S) emissions from typical municipal wastewater treatment plants (MWTPs) to accurately describe their emission rate estimates (EREs) using operating parameters. This research included laboratory and field assessments of greenhouse gas (GHG) and odour emissions in conjunction with monitored operating parameters. Laboratory-scale reactors simulating open-to-air treatment processes including primary and secondary clarifiers and anaerobic, anoxic, and aerobic reactors, were used to monitor gas EREs using wastewater samples taken from the analogous MWTP processes in winter and summer seasons. The Saskatoon Wastewater Treatment plan (SWTP) is a state-of-the-art biological nutrient removal (BNR) type MWTP and a Class IV treatment facility in Canada which was selected as a case study given its highly variable seasonal temperatures from −40 °C to 30 °C and its geographic location near the University of Saskatchewan. The experimental results were then used to develop a variety of novel machine learning models describing gas EREs with further optimization of operating parameters using genetic algorithm (GA). Studied machine learning models were artificial data generation algorithms (including generative adversarial network, GAN) and data-driven models (including artificial neural network, ANN; adaptive network-based fuzzy inference systems, ANFIS; and linear/non-linear regression models). To my knowledge, this is the first application of GAN used for MWTP modelling purposes. Results indicated that anaerobic digestion EREs averagely reached 4,443 kg CH4/d, 9,145 kg CO2/d, and 59.7 kg H2S/d. In contrast, GHG and odour ERE variabilities given ambient temperature changes were more noticeable for open-to-air treatment processes such that the winter EREs were 45,129 kg CO2/d, 21.9 kg CH4/d, 3.20 kg N2O/d, and insignificant for H2S and NH3. The higher temperature for the summer samples resulted in increased EREs for CH4, N2O, and H2S EREs of 33.0 kg CH4/d, 3.87 kg N2O/d, and 2.29 kg H2S/d, respectively, and still insignificant NH3 emissions. However, the CO2 EREs were reduced to 37,794 kg CO2/d, and interestingly, NH3 emissions were still negligible. Overall, the aerobic reactor was the dominant source of GHG emissions for both seasons, and changes in the aerobic reactor aeration rates (in reactor) and BNR treatment configurations (from site) further impacted the EREs. The integration of field monitoring data with data-driven models showed that the ANN, ANFIS, and regression models provided reasonable EREs using: (1) volatile fatty acids, total/fixed/volatile solids, pH, and inflow rate for anaerobic digestion biogas generations; and (2) hydraulic retention time, temperature, total organic carbon, dissolved oxygen, phosphate, and nitrogen concentrations for aerobic GHG emissions. However, when both model accuracy and uncertainty were considered there appears to be a compromise between these parameters with no model having simultaneously both high accuracy and low uncertainty. Additionally, and interestingly, virtual data augmentation using GAN was found to be a valuable resource in supplementation of limited data for improved modelling outcomes. GA was also coupled with the data-driven models to determine optimal operating parameters resulting in either GHG emission maximization given biogas could be beneficial for energy generation or GHG emission minimization given the aerobic reactor is an open-to-air process that can impact nearby residential neighbourhood air quality. The current study provides a hybrid methodology of mathematical modelling and experiments that can be used to accurately estimate and optimize the GHG and odour EREs from other MWTPs in Canada and worldwide

A systematic review of machine-learning solutions in anaerobic digestion

The use of machine learning (ML) in anaerobic digestion (AD) is growing in popularity and improves the interpretation of complex system parameters for better operation and optimisation. This systematic literature review aims to explore how ML is currently employed in AD, with particular attention to the challenges of implementation and the benefits of integrating ML techniques. While both lab and industry-scale datasets have been used for model training, challenges arise from varied system designs and the different monitoring equipment used. Traditional machine-learning techniques, predominantly artificial neural networks (ANN), are the most commonly used but face difficulties in scalability and interpretability. Specifically, models trained on lab-scale data often struggle to generalize to full-scale, real-world operations due to the complexity and variability in bacterial communities and system operations. In practical scenarios, machine learning can be employed in real-time operations for predictive modelling, ensuring system stability is maintained, resulting in improved efficiency of both biogas production and waste treatment processes. Through reviewing the ML techniques employed in wider applied domains, potential future research opportunities in addressing these challenges have been identified

IoT technology for smart Water system

A serious drop in ensuring the water quality in the distribution system is a factor that affects public health. This could lead to increase in biological and non-biological contents, change in colour and odour of the water. These contaminants cause a serious threat to the whole water ecosystem. The conventional methods of analyzing the water quality require much time and labour. So there is a need to monitor and protect the water with a real time water quality monitoring system in order to make active measurements to reduce contamination. The growth of the technology had helped in developing efficient methods to solve many serious issues in real-time. Internet of things (IoT) has achieved a great focus due to its faster processing and intelligence. This paper focuses on discussing the architecture, applications and need of IoT in water management syste

Synthesis of hydrous iron oxide/aluminum hydroxide composite loaded on coal fly ash as an effective mesoporous and low-cost sorbent for Cr(VI) sorption: Fuzzy logic modeling

The aim of this research was to estimate the possibility of using synthesized hydrous iron oxide/aluminium hydroxide composite loaded on coal fly ash (FA3) as an efficient sorbent for Cr(VI) sorption from aqueous solution. In this regard, dissolution and precipitation processes were performed to rearrange and load the intrinsic iron and aluminum on the surface of fly ash. Different characterization techniques including XRD, XRF, FT-IR, SEM, LPS and BET surface area were applied to analyze the sorbent properties. Moreover, sorption kinetics were studied using Morris–Weber intra-particle diffusion, Lagergren pseudo-first-order and pseudo-second-order models. The kinetic analyses indicated that pseudo-first-order model controlled the sorption process. In order to estimate the sorbent capacity, Langmuir, Freundlich and D–R models were applied. The thermodynamic parameters of Cr(VI) sorption were also studied. In addition, removal efficiency of Cr(VI) was predicted using the developed fuzzy logic model. The fuzzification of four input variables including pH, contact time, adsorbent dose and initial Cr(VI) concentration versus removal efficiency as output was carried out using an artificial intelligence-based approach. A Mamdani-type fuzzy interface system was employed to fulfill a collection of 24 rules (If-Then format) using triangle membership functions (MFS) with seven levels in fuzzy sets. The proposed fuzzy logic model demonstrated high predictive performance with correlation coefficient (R2) of 0.95 and acceptable deviation from the experimental data, confirming its suitability to predict Cr(VI) removal efficiency. Based on experimental data and statistical analysis, the synthetized sorbent was effective for treating wastewater containing Cr(VI).Peer ReviewedPostprint (published version

Study on performance enhancement of anaerobic digestion of municipal sewage sludge

This research focuses on investigating effects of combined microwave-ultrasonic pretreatment of municipal sewage sludge on anaerobic digester performance. Application of a novel combined microwave-ultrasonic pretreatment on thickened excess activated and mixed sludge significantly improved methane production, volatile solid and COD reduction, digestion kinetics, pathogen removal and dewaterability. Effects of Organic loading rate, sludge retention time and sludge mixing ratio were studied. Microwave and ultrasonic pretreatment conditions and digester operational parameters were optimized using experimental and modelling techniques

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