Data-driven models for predicting microbial water quality in the drinking water source using E. coli monitoring and hydrometeorological data

Rapid changes in microbial water quality in surface waters pose challenges for production of safe drinking water. If not treated to an acceptable level, microbial pathogens present in the drinking water can result in severe consequences for public health. The aim of this paper was to evaluate the suitability of data-driven models of different complexity for predicting the concentrations of E. coli in the river G\uf6ta \ue4lv at the water intake of the drinking water treatment plant in Gothenburg, Sweden. The objectives were to (i) assess how the complexity of the model affects the model performance; and (ii) identify relevant factors and assess their effect as predictors of E. coli levels. To forecast E. coli levels one day ahead, the data on laboratory measurements of E. coli and total coliforms, Colifast measurements of E. coli, water temperature, turbidity, precipitation, and water flow were used. The baseline approaches included Exponential Smoothing and ARIMA (Autoregressive Integrated Moving Average), which are commonly used univariate methods, and a naive baseline that used the previous observed value as its next prediction. Also, models common in the machine learning domain were included: LASSO (Least Absolute Shrinkage and Selection Operator) Regression and Random Forest, and a tool for optimising machine learning pipelines – TPOT (Tree-based Pipeline Optimization Tool). Also, a multivariate autoregressive model VAR (Vector Autoregression) was included. The models that included multiple predictors performed better than univariate models. Random Forest and TPOT resulted in higher performance but showed a tendency of overfitting. Water temperature, microbial concentrations upstream and at the water intake, and precipitation upstream were shown to be important predictors. Data-driven modelling enables water producers to interpret the measurements in the context of what concentrations can be expected based on the recent historic data, and thus identify unexplained deviations warranting further investigation of their origin

Sustainability ranking of desalination plants using Mamdani Fuzzy Logic Inference Systems

As water desalination continues to expand globally, desalination plants are continually under pressure to meet the requirements of sustainable development. However, the majority of desalination sustainability research has focused on new desalination projects, with limited research on sustainability performance of existing desalination plants. This is particularly important while considering countries with limited resources for freshwater such as the United Arab Emirates (UAE) as it is heavily reliant on existing desalination infrastructure. In this regard, the current research deals with the sustainability analysis of desalination processes using a generic sustainability ranking framework based on Mamdani Fuzzy Logic Inference Systems. The fuzzy-based models were validated using data from two typical desalination plants in the UAE. The promising results obtained from the fuzzy ranking framework suggest this more in-depth sustainability analysis should be beneficial due to its flexibility and adaptability in meeting the requirements of desalination sustainability

Performance assessment and optimisation of a novel guideless irregular dew point cooler using artificial intelligence

Air Conditioners (ACs) are a vital need in modern buildings to provide comfortable indoor air for the occupants. Several alternatives for the traditional coolers are introduced to improve the cooling efficiency but among them, Evaporative Coolers (ECs) absorbed more attention owing to their intelligible structure and high efficiency. ECs are categorized into two types, i.e., Direct Evaporative Coolers (DECs) and Indirect Evaporative Coolers (IECs). Continuous endeavours in the improvement of the ECs resulted in development of Dew Point Coolers (DPCs) which enable the supply air to reach the dew point temperature. The main innovation of DPCs relies on invention of a M-cycle Heat and Mass Exchanger (HMX) which contributes towards improvement of the ECs’ efficiency by up to 30%. A state-of-the-art counter flow DPC in which the flat plates in traditional HMXs are replaced by the corrugated plates is called Guideless Irregular DPC (GIDPC). This technology has 30-60% more cooling efficiency compared to the flat plate HMX in traditional DPCs.Owing to the empirical success of the Artificial Intelligence (AI) in different fields and enhanced importance of Machine Learning (ML) models, this study pioneers in developing two ML models using Multiple Polynomial Regression (MPR), and Deep Neural Network (DNN) methods, and three Multi Objective Evolutionary Optimisation (MOEO) models using Genetic Algorithms (GA), Particle Swarm Optimisation (PSO), and a novel bio-inspired algorithm, i.e., Slime Mould Algorithm (SMA), for the performance prediction and optimisation of the GIDPC in all possible operating climates. Furthermore, this study pioneers in developing an explainable and interpretable DNN model for the GIDPC. To this end, a game theory-based SHapley Additive exPlanations (SHAP) method is used to interpret contribution of the operating conditions on performance parameters.The ML models, take the intake air characteristic as well as main operating and design parameters of the HMX as inputs of the ML models to predict the GIDPC’s performance parameters, e.g., cooling capacity, coefficient of performance (COP), thermal efficiencies. The results revealed that both models have high prediction accuracies where MPR performs with a maximum average error of 1.22%. In addition, the Mean Square Error (MSE) of the selected DNN model is only 0.04. The objectives of the MOEO models are to simultaneously maximise the cooling efficiency and minimise the construction cost of the GIDPC by determining the optimum values of the selected decision variables.The performance of the optimised GIDPCs is compared in a deterministic way in which the comparisons are carried out in diverse climates in 2020 and 2050 in which the hourly future weather data are projected using a high-emission scenario defined by Intergovernmental Panel for Climate Change (IPCC). The results revealed that the hourly COP of the optimised systems outperforms the base design. Moreover, although power consumption of all systems increases from 2020 to 2050, owing to more operating hours as a result of global warming, but power savings of up to 72%, 69.49%, 63.24%, and 69.21% in hot summer continental, arid, tropical rainforest and Mediterranean hot summer climates respectively, can be achieved compared to the base system when the systems run optimally

Neural Networks for Flow Bottom Hole Pressure Prediction

Installation of down-hole gauges in oil wells to determine Flowing Bottom-Hole Pressure (FBHP) is a dominant process especially in wells lifted with electrical submersible pumps.  However, intervening a well occasionally is an exhaustive task, associated with production risk, and interruption. The previous empirical correlations and mechanistic models failed to provide a satisfactory and reliable tool for estimating pressure drop in multiphase flowing wells. This paper aims to find the optimum parameters of Feed-Forward Neural Network (FFNN) with back-propagation algorithm to predict the flowing bottom-hole pressure in vertical oil wells.  The developed neural network models rely on a large amount of available historical data measured from actual different oil fields. The unsurpassed number of neural network layers, the number of neurons per layer, and the number of trained samples required to get an outstanding performance have been obtained. Intensive experiments have been conducted and for the sake of qualitative comparison, Radial Basis neural and network and the empirical modes have been developed. The paper showed that the accuracy of FBHP estimation using FFNN with two hidden layer model is better than FFNN with single hidden layer model, Radial Basis neural network, and the empirical model in terms of data set used, mean square error, and the correlation coefficient error. With best results of 1.4 root mean square error (RMSE), 1.4 standard deviation of relative error (STD), correlation coefficient (R) 1.0 and 99.4% of the test data sets achieved less than 5% error. The minimum sufficient number of data sets used in training ANN model can be low as 12.5% of the total data sets to give 3.4 RMSE and 97% of the test data achieved 90% accuracy

A study on the introduction of artificial intelligence technology in the water treatment process

Thesis(Master) --KDI School:Master of Public Mangement,2020.Today, we stand in front of a huge wave of change named the "Fourth industrial revolution." Key technologies of the Fourth Industrial Revolution include artificial intelligence, the Internet of Thing (IoT), cloud computing, big data analysis, etc. These technologies will lead to an intelligent information society, and platform services will change every aspect of society from economic and work. This paper proposes several introductions of Artificial Intelligence Technology to improve water management. AI Technology secure a leadership position in the unfolding revolution and expedite the realization of an intelligent information company. K-water has to secure innovative technologies in advance as the foster related industries and upgrade services in order to generate new value and ensure the competitiveness of its intelligent water system. The K-water should take significant steps to thoroughly prepare for the coming Fourth Industrial Revolution, such as Artificial Intelligence-based autonomous Water Purification Plant with developing a creative water treatment process. The artificial intelligence system will be able to secure technological competitiveness in the water industry and secure future growth engines in the water industry by securing intelligence information technology, which is key to the fourth industrial revolution.Ⅰ. Introduction Ⅱ. Review of Literature and Cases III. Analysis of AI Technology Application in Water Treatment Ⅳ. Recommendation for the Standard Model of Artificial Intelligence Ⅴ. ConclusionmasterpublishedSeong Il, JEONG

MODELING AND EXPERIMENTAL SETUP OF AN HCCI ENGINE

For the past three decades the automotive industry is facing two main conflicting challenges to improve fuel economy and meet emissions standards. This has driven the engineers and researchers around the world to develop engines and powertrain which can meet these two daunting challenges. Focusing on the internal combustion engines there are very few options to enhance their performance beyond the current standards without increasing the price considerably. The Homogeneous Charge Compression Ignition (HCCI) engine technology is one of the combustion techniques which has the potential to partially meet the current critical challenges including CAFE standards and stringent EPA emissions standards. HCCI works on very lean mixtures compared to current SI engines, resulting in very low combustion temperatures and ultra-low NOx emissions. These engines when controlled accurately result in ultra-low soot formation. On the other hand HCCI engines face a problem of high unburnt hydrocarbon and carbon monoxide emissions. This technology also faces acute combustion controls problem, which if not dealt properly with yields highly unfavorable operating conditions and exhaust emissions. This thesis contains two main parts. One part deals in developing an HCCI experimental setup and the other focusses on developing a grey box modelling technique to control HCCI exhaust gas emissions. The experimental part gives the complete details on modification made on the stock engine to run in HCCI mode. This part also comprises details and specifications of all the sensors, actuators and other auxiliary parts attached to the conventional SI engine in order to run and monitor the engine in SI mode and future SI-HCCI mode switching studies. In the latter part around 600 data points from two different HCCI setups for two different engines are studied. A grey-box model for emission prediction is developed. The grey box model is trained with the use of 75% data and the remaining data is used for validation purpose. An average of 70% increase in accuracy for predicting engine performance is found while using the grey-box over an empirical (black box) model during this study. The grey-box model provides a solution for the difficulty faced for real time control of an HCCI engine. The grey-box model in this thesis is the first study in literature to develop a control oriented model for predicting HCCI engine emissions for control