Predictive Models for Prediction of Broad Crested Gabion Weir Aeration Performance

The gabion weirs serve the same functions that their counterpart impervious weirs do. However, they have the advantage of being eco-friendly, more stable, and economical in low to medium-head cases. Dissolved oxygen is one of the major determinants for the assessment of the purity of water. The purpose of the present work is to illustrate the comparison of multiple linear regression (MLR), neural network (NN), neuro-fuzzy system (NFS), deep neural network (DNN), and reported empirical models for the prediction of gabion weir aeration performance efficiency (APE20) with experimental results which are collected from the laboratory test. The NFS with four shaped membership functions, NN, DNN, MLR, and existing empirical models, are generated with the same input parameters, and their potentials are assessed to statistical appraisal indices. The results show that the DNN with the highest value of R2 (0.935) and NSE (0.934) and having the least errors in validating phase is the outperforming proposed model in the prediction of the APE20, which the NN model follows with R2 (0.917) and NSE (0.917). However, except trapezoidal shaped NFS model with R2 (0.873) and NSE (0.852) and MLR with R2 (0.905) and NSE (0.897), the remaining models of NFS-based and empirical relations could not perform better in validating phase. The sensitivity performance test is too conducted to find the relative relevance of the input parameter on the results of the APE20, where discharge per unit width (q) is found to be the most significant parameter, followed by the drop height (H0)

Modelling activated sludge wastewater treatment plants using artificial intelligence techniques (fuzzy logic and neural networks)

Activated sludge process (ASP) is the most commonly used biological wastewater treatment system. Mathematical modelling of this process is important for improving its treatment efficiency and thus the quality of the effluent released into the receiving water body. This is because the models can help the operator to predict the performance of the plant in order to take cost-effective and timely remedial actions that would ensure consistent treatment efficiency and meeting discharge consents. However, due to the highly complex and non-linear characteristics of this biological system, traditional mathematical modelling of this treatment process has remained a challenge. This thesis presents the applications of Artificial Intelligence (AI) techniques for modelling the ASP. These include the Kohonen Self Organising Map (KSOM), backpropagation artificial neural networks (BPANN), and adaptive network based fuzzy inference system (ANFIS). A comparison between these techniques has been made and the possibility of the hybrids between them was also investigated and tested. The study demonstrated that AI techniques offer viable, flexible and effective modelling methodology alternative for the activated sludge system. The KSOM was found to be an attractive tool for data preparation because it can easily accommodate missing data and outliers and because of its power in extracting salient features from raw data. As a consequence of the latter, the KSOM offers an excellent tool for the visualisation of high dimensional data. In addition, the KSOM was used to develop a software sensor to predict biological oxygen demand. This soft-sensor represents a significant advance in real-time BOD operational control by offering a very fast estimation of this important wastewater parameter when compared to the traditional 5-days bio-essay BOD test procedure. Furthermore, hybrids of KSOM-ANN and KSOM-ANFIS were shown to result much more improved model performance than using the respective modelling paradigms on their own.Damascus Universit

Regression-Based Models for Predicting Discharge Coefficient of Triangular Side Orifice

- This study introduced another technique to predict the discharge coefficient (Cd) of the triangular side orifice (TSO). This technique is based on the SPSS software as multiple linear regression (MLR) and multiple nonlinear regression (MNLR) models. These models were established using 570 experimental datasets, 70 and 30% for calibration and testing stages, respectively. These sets considered five non-dimensional parameters, including (orifice crest height, orifice length, orifice height, upstream flow depth, and Froude number of the main channel). Results showed that the MLR and MNLR models in the calibrating stage had higher determination coefficients and lower errors. In addition, the importance of the input parameters was investigated, showing that the orifice crest height and Froude number highly affect the discharge coefficient value by 36%. In the testing stage, the estimated discharge coefficient by the MLR and MNLR models stayed within the range ±12 and ‡5%, respectively, of the experimental values. The MNLR model demonstrated a high level of equivalence compared to previous studies, which provided a mathematical expression to easily predict the TSO\u27s discharge coefficient

Penetration depth of plunging liquid jets – A data driven modelling approach

© 2016 Elsevier Inc. In the case of impinging water jets or droplets, air entrainment processes are crucial to the casing design of hydraulic impulse turbines in the micro-hydro sector. To initiate first steps towards a precise prediction of the complex, multi-phase casing flow of impulse turbines, single aspects such as the penetration depth of impinging liquid jets have to be separated and fully understood. Existing investigations determining penetration depths are related to a very small range of flow rates and therefore show an underestimation of the penetration depth being applied to the casing flow of impulse turbines, which are generally operated at higher flow rates. For a more general description of the air entrainment process, investigations of plunging water jets within an extended flow rate range are conducted and the penetration depth is modelled using a data driven artificial neural network (ANN) approach and a non-linear regression model.At low flow rates, experiments results are in accordance with existing studies, whereas penetration depths up to 170 cm are measured at higher flow rates. For the mathematical models to achieve a wide range applicability, a large data base is used, including published and measured data. The modelled penetration depths can be precisely verified by the performed measurements and show correct physical behaviour, even in areas without underlying data. Calculation rules, weight matrices and biases of the trained ANN are published to achieve high transparency and scientific improvement in neural modelling of penetration depths of impinging liquid jets

A least squares support vector machine model for prediction of the next day solar insolation for effective use of PV systems

Accurate prediction of daily solar insolation has been one of the most important issues of solar engineering. The amount of solar insolation on a given location is a vital data for photovoltaic plants. Systems efficiency is easily affected by the changes in solar radiation so, this study is aimed to develop a Least Squares Support Vector Machine (LS-SVM) based intelligent model to predict the next day’s solar insolation for taking measures. Daily temperature and insolation data measured by Turkish State Meteorological Service for three years (2000–2002) were used as training data and the values of 2003 used as testing data. Numbers of the days from 1st January, daily mean temperature, daily maximum temperature, sunshine duration and the solar insolation of the day before parameters have been used as inputs to predict the daily solar insolation. The simulations were carried out with SVM Toolbox of MATLAB software. As a conclusion the results show that LS-SVM is a good method in estimating the amount of solar insolation of a given location with 99.294% accuracy

Estimation of CO2 solubility in aqueous solutions of commonly used blended amines: Application to optimised greenhouse gas capture

One of the key concerns in the 21st century, alongside the growing population, is the increase in energy consumption and the resulting global warming. The impact of CO2, a prominent greenhouse gas, has garnered significant attention in the realm of CO2 capture and gas purification. CO2 absorption can be enhanced by introducing some additives into the aqueous solution. In this study, the accuracies of some of the most up-to-date computational approaches are investigated. The employed machine learning methods are hybrid-adaptive neuro-fuzzy inference system (Hybrid-ANFIS), particle swarm optimization-adaptive neuro-fuzzy inference system (PSO-ANFIS), least-squares support vector machines (LSSVM) and genetic algorithm-radial basis function (GA-RBF). The developed models were used in estimating the solubility of CO2 in binary and ternary amines aqueous solutions. i.e. blends of monoethanolamine (MEA), triethanolamine (TEA), aminomethyl propanol (AMP), and methyldiethanolamine (MDEA). This modeling study was undertaken over relatively significant ranges of CO2 loading (mole of CO2/mole of solution) as a function of input parameters, which are 0.4–2908 kPa for pressure, 303–393.15 K for temperature, 36.22–68.89 g/mol for apparent molecular weight, and 30–55 wt % for total concentration. In this work, the validity of approaches based on different statistical graphs was investigated, and it was observed that the developed methods, especially the GA-RBF model, are highly accurate in estimating the data of interest. The obtained AARD% values for the developed models are 18.63, 8.25, 12.22, and 7.54 for Hybrid-ANFIS, PSO-ANFIS, LSSVM, and GA-RBF, respectively

Water treatment analysis guide

The treatment of water up to potable/drinkable standards is a complex process, with many variables and parameters impacting on each other. In South Africa drinking water delivered to consumers must meet the requirements as recorded in the South African National Standards (SANS). Today, more so than ever, there are a number water sources that can be exploited and treated to provide safe drinking water, namely; surface water (dams and rivers), sea water, ground water and treated wastewater. The focus of this dissertation is on surface water; however, reference is made in the first sections with regards to sea water and ground water. The first step in designing a treatment process begins with analysis of the raw water source. Unfortunately, there is not a one size fits all approach and it is left up to the process engineer to find the correct method of investigation. This can be a daunting task, especially if lacking in experience and available information. The first part of this dissertation focusses on just that. It prescribes the method of sampling and aims to provide the reader with context on when to and what to test for. It goes further to suggest how the results may influence the process design and how certain contaminants can be removed. It also draws the attention to the sampling timeframe required, to obtain representative information, encompassing fluctuations in water quality. The second part of this dissertation describe the methods for designing a conventional water treatment system, comprising; aeration, coagulation, flocculation, dissolved air floatation, sedimentation, filtration and disinfection. It also comments on the water quality that warrants certain process steps to assist the process engineer in choosing the correct configuration. For most steps the design approach of two or more technologies are presented. This allows the process engineer to consider which technology best suits the application at hand. The design procedures are programmed into an, excel based, software model, which permits quick and easy design. A brief description of how the software model can be used is also covered. The results given by the software model is validated through a set of examples, appended to this document. Ultimately it is concluded that although this dissertation provides a guide for designing a treatment process it is not an encompassing tool that considers all the intricacies involved. That is, there are too many factors involved and considerations required, and cannot all be captured in one dissertation such as this. As such, it is finally recommended that any design attempts should be conducted by a suitably qualified and experienced process engineer that may use this dissertation to augment their design development