scholarly journals C-Language Programming for Development of Conventional Water Treatment Plants Decision Support System

2014 ◽  
Vol 03 (04) ◽  
pp. 129-139
Author(s):  
Thogare N. Shridhara ◽  
Samson O. Ojoawo ◽  
Pilli V. Mahaganesha ◽  
Mallaura R. Thippeswary ◽  
Rahul Anand ◽  
...  
Keyword(s):  
Decision Support ◽  
Water Treatment ◽  
Decision Support System ◽  
Support System ◽  
C Language ◽  
Water Treatment Plants

Development of decision support system for emergency management in water treatment plants

2020 ◽  
Vol 191 ◽  
pp. 40-50
Author(s):  
Jaehyun Ju ◽  
Yongjun Choi ◽  
Jihyeok Choi ◽  
Youngkyu Park ◽  
Sangho Lee
Keyword(s):  
Decision Support ◽  
Water Treatment ◽  
Decision Support System ◽  
Emergency Management ◽  
Support System ◽  
Water Treatment Plants

Performance management of small water treatment plant operations: a decision support system

2017 ◽  
Vol 31 (3) ◽  
pp. 330-344 ◽  
Author(s):  
Dan Stein ◽  
Gopal Achari ◽  
Cooper H. Langford ◽  
Mohammed H. I. Dore ◽  
Husnain Haider ◽  
...  
Keyword(s):  
Decision Support ◽  
Water Treatment ◽  
Decision Support System ◽  
Performance Management ◽  
Support System ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Small Water ◽  
Plant Operations

scholarly journals Implementation of an environmental decision support system for controlling the pre-oxidation step at a full-scale drinking water treatment plant

2020 ◽  
Vol 81 (8) ◽  
pp. 1778-1785 ◽  
Author(s):  
Lluís Godo-Pla ◽  
Pere Emiliano ◽  
Santiago González ◽  
Manel Poch ◽  
Fernando Valero ◽  
...  
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Decision Support ◽  
Water Treatment ◽  
Decision Support System ◽  
Predictive Model ◽  
Support System ◽  
Drinking Water Treatment ◽  
Oxidation Step ◽  
Environmental Decision

Abstract Drinking water treatment plants (DWTPs) face changes in raw water quality, and treatment needs to be adjusted to produce the best water quality at the minimum environmental cost. An environmental decision support system (EDSS) was developed for aiding DWTP operators in choosing the adequate permanganate dosing rate in the pre-oxidation step. To this end, multiple linear regression (MLR) and multi-layer perceptron (MLP) models are compared for choosing the best predictive model. Besides, a case-based reasoning (CBR) model was approached to provide the user with a distribution of solutions given similar operating conditions in the past. The predictive model consisted of an MLP and has been validated against historical data with sufficient good accuracy for the utility needs (R2 = 0.76 and RSE = 0.13 mg·L−1). The integration of the predictive and the CBR models in an EDSS gives the user an augmented decision-making capacity of the process and has great potential for both assisting experienced users and for training new personnel in deciding the operational set-point of the process.

scholarly journals Decision Support System for Determining Textile Industry Waste Water Treatment Institution in Web Based Citarum River

Jurnal METRIS ◽  
2021 ◽  
Vol 20 (1) ◽  
pp. 23-32
Author(s):  
Siska Febriyanti ◽  
Augustina Asih Rumanti ◽  
Nurdinintya Athari Supratman
Keyword(s):  
Waste Water ◽  
Decision Support ◽  
Water Treatment ◽  
Decision Support System ◽  
Support System ◽  
Textile Industry ◽  
Waste Water Treatment ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Waste Water Treatment Plant

The Citarum River is the longest river in West Java. The river participates in the development of the Indonesian economy by 20% of GDP (Gross Domestic Product). In 2018, the citarum river ecosystem structuring survey team found 31 factories in the Bandung Regency region that dumped the waste produced directly into the Citarum River, one of which was the textile industry. In the production process the textile industry uses textile dyes containing azo dyes. The compound has the potential to produce aminobenzen or aniline which causes pollution. The lack of a communal Waste Water Treatment Plant (IPAL) and improper location is a factor that causes the industry to dispose of production waste water directly into the Citarum River. This study aims to support government activities in improving the Citarum River by designing a Decision Support System (DSS) using a website-based Analytical Hierarchy Process (AHP) method to determine the right location to build a communal WWTP. .

scholarly journals Development of an Environmental Decision Support System for Enhanced Coagulation in Drinking Water Production

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2115 ◽  
Author(s):  
Jordi Suquet ◽  
Lluís Godo-Pla ◽  
Meritxell Valentí ◽  
Marta Verdaguer ◽  
Maria J. Martin ◽  
...  
Keyword(s):  
Drinking Water ◽  
Decision Support ◽  
Water Treatment ◽  
Decision Support System ◽  
Support System ◽  
Control Level ◽  
Drinking Water Treatment ◽  
Water Production ◽  
Drinking Water Production ◽  
Environmental Decision

Drinking water production is subject to multiple water quality requirements such as minimizing disinfection byproducts (DBPs) formation, which are highly related to natural organic matter (NOM) content. For water treatment, coagulation is a key process for removing water pollutants and, as such, is widely implemented in drinking water treatment plants (DWTPs) facilities worldwide. In this context, artificial intelligence (AI) tools can be used to aid decision making. This study presents an environmental decision support system (EDSS) for coagulation in a Mediterranean DWTP. The EDSS is structured hierarchically into the following three levels: data acquisition, control, and supervision. The EDSS relies on influent water characterization, suggesting an optimal pH and coagulant dose. The model designed for the control level is based on response surface methodology (RSM), targeted to optimize removal for the response variables (turbidity, total organic carbon (TOC), and UV254). Results from the RSM model provided removal percentages for turbidity (64.6%), TOC (21.9%), and UV254 (30%), which represented an increase of 4%, 33%, and 28% as compared with the DWTP water sample. Regarding the entire EDSS, 62%, 21%, and 25% of turbidity, TOC, and UV254 removal were fixed as the optimization criteria. Supervision rules (SRs) were included at the top of the architecture to intensify process performance under specific circumstances.

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