scholarly journals An optimized PSO-ANN model for improved prediction of water treatment desalination plant performance

2021 ◽  
Author(s):  
R. Mahadeva ◽  
M. Kumar ◽  
S. P. Patole ◽  
G. Manik
Keyword(s):  
Water Treatment ◽  
Soft Computing ◽  
Accurate Prediction ◽  
Plant Performance ◽  
Actual Process ◽  
Ann Model ◽  
Soft Computing Techniques ◽  
Accurate Performance ◽  
Hidden Layer ◽  
Desalination Plants

Abstract An accurate prediction of the performance of water treatment desalination plants could directly improve the global socio-economic balance. In this regard, many researchers have been engaged in the various artificial intelligence applied soft computing techniques to predict actual process outcomes. Inspired by the significance of such techniques, an optimized Particle Swarm Optimization based Artificial Neural Network (PSO-ANN) technique has been proposed herewith to predict an accurate performance of the reverse osmosis (RO) based water treatment desalination plants. Literature suggests that the improvements of the soft computing models depend on their modeling parameters. Therefore, we have included an extended list of nine modeling parameters with a systematic indepth investigation to explore their optimal values. Finally, the model's simulations results (R2 = 99.1%, Error = 0.006) were found superior than the existing ANN models (R2 = 98.8%, Error = 0.060), with the same experimental datasets. Additionally, the simulation results recommend that among many parameters considered, the number of hidden layer nodes (n), swarm sizes (SS), and the weight of inertia (ω) play a major role in the model optimization. This study for a more accurate prediction of the plant's performance shall pave the way for the process design and control engineers to improve the plant efficiency further.

scholarly journals Urban Water Demand Forecasting: A Comparative Evaluation of Conventional and Soft Computing Techniques

Resources ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 156 ◽  
Author(s):  
Oluwaseun Oyebode ◽  
Desmond Eseoghene Ighravwe
Keyword(s):  
Soft Computing ◽  
Predictive Models ◽  
Water Demand ◽  
Demand Forecasting ◽  
Support Vector ◽  
Ann Model ◽  
Real World Data ◽  
Water Demand Forecasting ◽  
Soft Computing Techniques ◽  
Computing Models

Previous studies have shown that soft computing models are excellent predictive models for demand management problems. However, their applications in solving water demand forecasting problems have been scantily reported. In this study, feedforward artificial neural networks (ANNs) and a support vector machine (SVM) were used to forecast water consumption. Two ANN models were trained using different algorithms: differential evolution (DE) and conjugate gradient (CG). The performance of these soft computing models was investigated with real-world data sets from the City of Ekurhuleni, South Africa, and compared with conventionally used exponential smoothing (ES) and multiple linear regression (MLR). The results obtained showed that the ANN model that was trained with DE performed better than the CG-trained ANN and other predictive models (SVM, ES and MLR). This observation further demonstrates the robustness of evolutionary computation techniques amongst soft computing techniques.

scholarly journals Rainfall-Runoff Modeling of Sutlej River Basin (India) Using Soft Computing Techniques

2019 ◽  
Vol 10 (2) ◽  
pp. 1-20 ◽  
Author(s):  
Athar Hussain ◽  
Jatin Kumar Singh ◽  
A. R. Senthil Kumar ◽  
Harne K R
Keyword(s):  
Fuzzy Logic ◽  
Soft Computing ◽  
Spatial And Temporal Variability ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Runoff Modeling ◽  
Soft Computing Techniques ◽  
Scatter Plots ◽  
Important Input ◽  
Water Resources Projects

The prediction of the runoff generated within a watershed is an important input in the design and management of water resources projects. Due to the tremendous spatial and temporal variability in precipitation, rainfall-runoff relationship becomes one of the most complex hydrologic phenomena. Under such circumstances, using soft computing approaches have proven to be an efficient tool in modeling of runoff. These models are capable of predicting river runoff values that can be used for hydrologic and hydraulic engineering design and water management purposes. It has been observed that the artificial neural networks (ANN) model performed well compared to other soft computing techniques such as fuzzy logic and radial basis function investigated in this study. In addition, comparison of scatter plots indicates that the values of runoff predicted by the ANN model are more precise than those found by RBF or Fuzzy Logic model.

scholarly journals SRGM Dependent Learning based Testing Effort for Refinement

2021 ◽  
Vol 9 (11) ◽  
pp. 356-360
Author(s):  
S. Rumana Firdose
Keyword(s):  
Soft Computing ◽  
Software Reliability ◽  
The Other ◽  
Ann Model ◽  
Software Failure ◽  
Failure Data ◽  
Wide Range ◽  
Soft Computing Techniques ◽  
Computational Errors ◽  
Dependent Learning

Abstract: During the development of software code there is a pressing necessity to remove the faults or bugs and improve software reliability. To get the accurate result, in every phase of software development cycle assessments needs to be happen, so that in each phase early bugs detection takes place that leads to maintain accuracy at each level. The academic institutions and industries are enhancing the development techniques in software engineering and their by performing regular testing for finding faults in programmers of software during the development. New programs are composed by altered the original code by comprised more of a bias near statements that arise in pessimistic execution paths. Fault localization information technique is used in proposed method to indicate the position of fault. In experimental as well as regression based equations represent the soft computing techniques results is better compare to the other techniques. Evaluation of soft-computing techniques represented that accuracy of the ANN model is superior to the other models. Data bases for performing the training and testing stages were collected, these soft computing techniques had low computational errors than the empirical equations. Finally says that soft computing models are better compare to the regression models. Hence, finding faults and correcting a serious software problem would be better instead of recalling thousands of products, especially in automotive sector. SRGM success mainly reliable by gathering the accurate failure information. The functions of the software reliability growth model were predicted in terms of such information gathered only. SRGM techniques in the literature and it gives a reasonable capability of value for actual software failure data. Therefore, this model, in future, can be applied to operate a wide range of software and its applications. Keywords: SRGM, FDP, FCP

Impacts of separate rejection water treatment on the overall plant performance

2003 ◽  
Vol 48 (4) ◽  
pp. 139-146 ◽  
Author(s):  
B. Wett ◽  
J. Alex
Keyword(s):  
Water Treatment ◽  
Treatment Plant ◽  
Operating Experience ◽  
Plant Performance ◽  
High Tech ◽  
State Variables ◽  
Total N ◽  
Unit Process ◽  
Innovative Tool ◽  
The Impact

A separate rejection water treatment appears as a high-tech unit process which might be recommendable only for specific cases of an upgrading of an existing wastewater treatment plant. It is not the issue of this paper to consider a specific separate treatment process itself but to investigate the influence of such a process on the overall plant performance. A plant-wide model has been applied as an innovative tool to evaluate effects of the implemented sidestream strategy on the mainstream treatment. The model has been developed in the SIMBA environment and combines acknowledged mathematical descriptions of the activated sludge process (ASM1) and the anaerobic mesophilic digestion (Siegrist model). The model's calibration and validation was based on data from 5 years of operating experience of a full-scale rejection water treatment. The impact on the total N-elimination efficiency is demonstrated by detailed nitrogen mass flow schemes including the interactions between the wastewater and the sludge lane. Additionally limiting conditions due to dynamic N-return loads are displayed by the model's state variables.

One-step models for soft computing techniques. Industrial application to image processing in quality assurance process

2015 ◽  
Vol 81 (5-8) ◽  
pp. 771-778 ◽  
Author(s):  
Pascual Noradino Montes Dorantes ◽  
Marco Aurelio Jiménez Gómez ◽  
Gerardo Maximiliano Méndez ◽  
Juan Pablo Nieto González ◽  
Jesús de la Rosa Elizondo
Keyword(s):  
Image Processing ◽  
Quality Assurance ◽  
Soft Computing ◽  
Industrial Application ◽  
Quality Assurance Process ◽  
Soft Computing Techniques ◽  
One Step ◽  
Assurance Process
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