A novel approach for water quality management in water distribution systems by multi-objective booster chlorination

Copyright © 2012 International Journal of Civil EngineeringCompared to conventional chlorination methods which apply chlorine at water treatment plant, booster chlorination has almost solved the problems of high dosages of chlorine residuals near water sources and lack of chlorine residuals in the remote points of a water distribution system (WDS). However, control of trihalomethane (THM) formation as a potentially carcinogenic disinfection by-product (DBP) within a WDS has still remained as a water quality problem. This paper presents a two-phase approach of multi-objective booster disinfection in which both chlorine residuals and THM formation are concurrently optimized in a WDS. In the first phase, a booster disinfection system is formulated as a multi-objective optimization problem in which the location of booster stations is determined. The objectives are defined as to maximize the volumetric discharge with appropriate levels of disinfectant residuals throughout all demand nodes and to minimize the total mass of disinfectant applied with a specified number of booster stations. The most frequently selected locations for installing booster disinfection stations are selected for the second phase, in which another two-objective optimization problem is defined. The objectives in the second problem are to minimize the volumetric discharge avoiding THM maximum levels and to maximize the volumetric discharge with standard levels of disinfectant residuals. For each point on the resulted trade-off curve between the water quality objectives optimal scheduling of chlorination injected at each booster station is obtained. Both optimization problems used NSGA-II algorithm as a multi-objective genetic algorithm, coupled with EPANET as a hydraulic simulation model. The optimization problems are tested for different numbers of booster chlorination stations in a real case WDS. As a result, this type of multi-objective optimization model can explicitly give the decision makers the optimal location and scheduling of booster disinfection systems with respect to the trade-off between maximum safe drinking water with allowable chlorine residual levels and minimum adverse DBP levels

Event-based decision support algorithm for real-time flood forecasting in urban drainage systems using machine learning modelling

Urban flooding is a major problem for cities around the world, with significant socio-economic consequences. Conventional real-time flood forecasting models rely on continuous time-series data and often have limited accuracy, especially for longer lead times than 2 hrs. This study proposes a novel event-based decision support algorithm for real-time flood forecasting using event-based data identification, event-based dataset generation, and a real-time decision tree flowchart using machine learning models. The results of applying the framework to a real-world case study demonstrate higher accuracy in forecasting water level rise, especially for longer lead times (e.g., 2–3 hrs), compared to traditional models. The proposed framework reduces root mean square error by 50%, increases accuracy of flood forecasting by 50%, and improves normalised Nash–Sutcliffe error by 20%. The proposed event-based dataset framework can significantly enhance the accuracy of flood forecasting, reducing the occurrences of both false alarms and flood missing and improving emergency response systems

Developing a smart and clean technology for bioremediation of antibiotic contamination in arable lands

This study presents a smart technological framework to efficiently remove azithromycin from natural soil resources using bioremediation techniques. The framework consists of several modules, each with different models such as Penicillium Simplicissimum (PS) bioactivity, soft computing models, statistical optimisation, Machine Learning (ML) algorithms, and Decision Tree (DT) control system based on Removal Percentage (RP). The first module involves designing experiments using a literature review and the Taguchi Orthogonal design method for cultural conditions. The RP is predicted as a function of cultural parameters using Response Surface Methodology (RSM) and three ML algorithms: Instance-Based K (IBK), KStar, and Locally Weighted Learning (LWL). The sensitivity analysis shows that pH is the most important factor among all parameters, including pH, Aeration Intensity (AI), Temperature, Microbial/Food (M/F) ratio, and Retention Time (RT), with a p-value of <0.0001. AI is the next most significant parameter, also with a p-value of <0.0001. The optimal biological conditions for removing azithromycin from soil resources are a temperature of 32 °C, pH of 5.5, M/F ratio of 1.59 mg/g, and AI of 8.59 m3/h. During the 100-day bioremediation process, RP was found to be an insignificant factor for more than 25 days, which simplifies the conditions. Among the ML algorithms, the IBK model provided the most accurate prediction of RT, with a correlation coefficient of over 95%

Enhancing urban flood forecasting in drainage systems using dynamic ensemble-based data mining

This is the final version. Available on open access from Elsevier via the DOI in this recordData availability: Data will be made available on request.This study presents a novel approach for urban flood forecasting in drainage systems using a dynamic ensemble-based data mining model which has yet to be utilised properly in this context. The proposed method incorporates an event identification technique and rainfall feature extraction to develop weak learner data mining models. These models are then stacked to create a time-series ensemble model using a decision tree algorithm and confusion matrix-based blending method. The proposed model was compared to other commonly used ensemble models in a real-world urban drainage system in the UK. The results show that the proposed model achieves a higher hit rate compared to other benchmark models, with a hit rate of around 85% vs 70 % for the next 3 h of forecasting. Additionally, the proposed smart model can accurately classify various timesteps of flood or non-flood events without significant lag times, resulting in fewer false alarms, reduced unnecessary risk management actions, and lower costs in real-time early warning applications. The findings also demonstrate that two features, “antecedent precipitation history” and “seasonal time occurrence of rainfall,” significantly enhance the accuracy of flood forecasting with a hit rate accuracy ranging from 60 % to 10 % for a lead time of 15 min to 3 h.Devon Resilience Innovation Project (DRIP

Decision process in large-scale crisis management

International audienceThis paper deals with the decision-aiding process in large-scale crisis such as natural disasters. It consists in four phases: decision context characterization, system modelling, aggregation and integration. The elements of the context, such as crisis level, risk situation, decision-maker problem issue are defined through the characterization phase. At the feared event occurrence, these elements will interact on a target system. Through the model on this system, the consequences to stakes could be assessed or estimated. The presented aggregation approaches will allow taking the right decisions. The architecture of a Decision Support System is presented in the integration phase

Feasibility of hydraulic separation in a novel anaerobic-anoxic upflow reactor for biological nutrient removal

ABSTRACT : This contribution deals with a novel anaerobic-anoxic reactor for biological nutrient removal (BNR) from wastewater, termed AnoxAn. In the AnoxAn reactor, the anaerobic and anoxic zones for phosphate removal and denitrification are integrated in a single continuous upflow sludge blanket reactor, aiming at high compactness and efficiency. Its application is envisaged in those cases where retrofitting of existing wastewater treatment plants for BNR, or the construction of new ones, is limited by the available surface area. The environmental conditions are vertically divided up inside the reactor with the anaerobic zone at the bottom and the anoxic zone above. The capability of the AnoxAn configuration to establish two hydraulically separated zones inside the single reactor was assessed by means of hydraulic characterization experiments and model simulations. Residence time distribution (RTD) experiments in clean water were performed in a bench-scale (48.4 L) AnoxAn prototype. The required hydraulic separation between the anaerobic and anoxic zones, as well as adequate mixing in the individual zones, was obtained through selected mixing devices. The observed behaviour was described by a hydraulic model consisting of continuous stirred tank reactors and plug-flow reactors. The impact of the denitrification process in the anoxic zone on the hydraulic separation was subsequently evaluated through model simulations. The desired hydraulic behaviour proved feasible, involving little mixing between the anaerobic and anoxic zones (mixing flowrate 40.2% of influent flowrate) and negligible nitrate concentration in the anaerobic zone (less than 0.1 mgN L-1) when denitrification was considered

Theories of schizophrenia: a genetic-inflammatory-vascular synthesis

BACKGROUND: Schizophrenia, a relatively common psychiatric syndrome, affects virtually all brain functions yet has eluded explanation for more than 100 years. Whether by developmental and/or degenerative processes, abnormalities of neurons and their synaptic connections have been the recent focus of attention. However, our inability to fathom the pathophysiology of schizophrenia forces us to challenge our theoretical models and beliefs. A search for a more satisfying model to explain aspects of schizophrenia uncovers clues pointing to genetically mediated CNS microvascular inflammatory disease. DISCUSSION: A vascular component to a theory of schizophrenia posits that the physiologic abnormalities leading to illness involve disruption of the exquisitely precise regulation of the delivery of energy and oxygen required for normal brain function. The theory further proposes that abnormalities of CNS metabolism arise because genetically modulated inflammatory reactions damage the microvascular system of the brain in reaction to environmental agents, including infections, hypoxia, and physical trauma. Damage may accumulate with repeated exposure to triggering agents resulting in exacerbation and deterioration, or healing with their removal. There are clear examples of genetic polymorphisms in inflammatory regulators leading to exaggerated inflammatory responses. There is also ample evidence that inflammatory vascular disease of the brain can lead to psychosis, often waxing and waning, and exhibiting a fluctuating course, as seen in schizophrenia. Disturbances of CNS blood flow have repeatedly been observed in people with schizophrenia using old and new technologies. To account for the myriad of behavioral and other curious findings in schizophrenia such as minor physical anomalies, or reported decreased rates of rheumatoid arthritis and highly visible nail fold capillaries, we would have to evoke a process that is systemic such as the vascular and immune/inflammatory systems. SUMMARY: A vascular-inflammatory theory of schizophrenia brings together environmental and genetic factors in a way that can explain the diversity of symptoms and outcomes observed. If these ideas are confirmed, they would lead in new directions for treatments or preventions by avoiding inducers of inflammation or by way of inflammatory modulating agents, thus preventing exaggerated inflammation and consequent triggering of a psychotic episode in genetically predisposed persons

A mixed-data evaluation in group TOPSIS with differentiated decision power

[[abstract]]This main objective of this paper is to provide decision support for mixed data in group Technique for Order Preference by Similarity to Idea Solution (TOPSIS) with differentiated decision power. We use a signum function to compare the ordinal performance of alternatives on any qualitative criterion, or the partial information provided by decision makers. The proposed process for ordinal information is uniformly coherent with the traditional TOPSIS steps, preserving the characteristic of distance-based utilities. Ordinal weights are also considered herein, and the decision power of the group members is formulated by their weights under an agreement in the group. Two examples demonstrate that the proposed approach has some benefits and achieves robustness with two types of sensitivity analyses. Some discussions and their limitations to the approach are also provided.[[notice]]補正完