Artificial intelligence for greywater treatment using electrocoagulation process

Nasr, Mahmoud; Ateia, Mohamed; Hassan, Kareem

doi:10.1080/01496395.2015.1062399

Cited by 44 publications

(18 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These equations are found in [22]. The whole data set was organized into three groups: a training group (70%), a validation group (15%) and a test group (15%) [12], as shown in Table 2. Finally, in order to determine the number of hidden neurons, iterative tests were performed for a group of neural networks and the mean square error (MSE) was measured.…”

Section: Ann Modeling and Optimizationmentioning

confidence: 99%

“…In the literature, studies have reported using artificial intelligence, such as artificial neural networks (ANNs), in the treatment of wastewater applying the (EC) method. ANN is a computational technique that reproduces the biological processing capacity of the human brain [12]. Another technique for modeling systems is the response surface methodology (RSM), which has been used to model the treatment of textile dyeing factory wastewater by EC [13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling and Optimization of COD Removal from Cold Meat Industry Wastewater by Electrocoagulation Using Computational Techniques

et al. 2020

View full text Add to dashboard Cite

In this paper, electrocoagulation (EC) treatment for the removal of chemical oxygen demand (COD) from cold meat industry wastewater is modeled and optimized using computational techniques. Methods such as artificial neural networks (ANNs) and response surface methodology (RSM), based on the Box–Behnken design using three levels, were employed to calculate the best control parameters for pH (5–9), current density (2–6 mA/cm2) and EC time (20–60 min). Analysis of variance (ANOVA) and 3D graphs revealed that pH and current density are the main parameters used for depicting the EC process. The developed models successfully describe the process with a correlation coefficient of R2 = 0.96 for RSM and R2 = 0.99 for ANN. The models obtained were optimized applying the moth-flame optimization (MFO) algorithm to find the best operating conditions for COD removal. ANN-MFO was used and showed superior COD removal (92.91%) under conditions of pH = 8.9, current density = 6.6 mA/cm2 and an EC time of 38.62 min. The energy consumption with these optimal conditions was 6.92 kWh/m3, with an operational cost of $3.14 (USD)/m3. These results suggest that the proposed computational model can be used to obtain more effective and economical treatments for this type of effluent.

show abstract

Section: Ann Modeling and Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling and Optimization of COD Removal from Cold Meat Industry Wastewater by Electrocoagulation Using Computational Techniques

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Similarly, in this study, a specialized artificial neural network (ANN) was applied to process the sensory data from FSRs for detecting different sitting behaviors of the user. In the LightSit system, we applied a conventional three-layer (an input layer, a hidden layer, an output layer) feedforward network using the Levenberg–Marquardt algorithm, which has been widely applied and validated in a variety of ANN applications, such as greywater treatment [46], skin lesion analysis [47], posture classification [45], etc.…”

Section: Design Of Lightsitmentioning

confidence: 99%

LightSit: An Unobtrusive Health-Promoting System for Relaxation and Fitness Microbreaks at Work

Ren

Lü

et al. 2019

Sensors

View full text Add to dashboard Cite

Physical inactivity and chronic stress at work increase the risks of developing metabolic disorders, mental illnesses, and musculoskeletal injuries, threatening office workers’ physical and psychological well-being. Although several guidelines and interventions have been developed to prevent theses subhealth issues, their effectiveness and health benefits are largely limited when they cannot match workday contexts. This paper presents LightSit, a health-promoting system that helps people reduce physically inactive behaviors and manage chronic stress at work. LightSit comprises a sensor mat that can be embedded into an office chair for measuring a user’s sitting posture and heart rate variability and a lighting display that is integrated into a monitor stand to present information unobtrusively, facilitating fitness and relaxation exercises during microbreaks. Following the showroom approach, we evaluated LightSit during a public exhibition at Dutch Design Week 2018. During the eight days of the exhibition, we observed more than 500 sessions of experiences with healthy microbreaks using our prototype. Semistructured interviews were conducted with 50 participants who had office-based jobs and had experienced LightSit. Our qualitative findings indicated the potential benefits of LightSit in facilitating health-promoting behaviors during office work. Based on the insights learned from this study, we discuss the implications for future designs of interactive health-promoting systems.

show abstract

“…Several methodologies and tools using artificial intelligence (AI) can be an alternative to replace the conventional phenomenological models for data-driven models such as artificial neural networks (ANN) (Nasr et al 2016;Bock et al 2019;Morales-Rivera et al 2020). ANNs are advanced machine learning algorithms suitable for fitting and pattern recognition, allowing the extraction of complex relationships from a set of linear and nonlinear input variables to predict target outputs (Sivanandam & Deepa 2006).…”

Section: Introductionmentioning

confidence: 99%

Modeling and optimization of a continuous electrocoagulation process using an artificial intelligence approach

2021

View full text Add to dashboard Cite

An artificial neural network (ANN) with the topology 8-94-85-2 (input – hidden layer 1 - hidden layer 2 - output) was used to model the operation of the continuous electrocoagulation (CEC) process for the removal of fluoride from water. After the ANN training, the sum of the squared errors (MSE) and the determination coefficient (R2) of the testing set model predictions were 0.0088 and 0.999, respectively, showing a good generalization and model's predictive capacity. The optimization of the process cost using the genetic algorithm (GA) showed that the optimal conditions are highly dependent on the feed concentration and the fluoride removal requirements. For a 5 L of water containing 10 mg/L of fluoride, the optimal conditions to reduce the fluoride concentration below the permissible limit (1.5 mg/L) are 88.3 mA of current intensity, a flow rate of 73.6 mL/min, and the use of a series monopolar (SM) electrode configuration, corresponding to a fluoride removal of 85% and an operating cost of 0.05 €/L.

show abstract

Artificial intelligence for greywater treatment using electrocoagulation process

Cited by 44 publications

References 23 publications

Modeling and Optimization of COD Removal from Cold Meat Industry Wastewater by Electrocoagulation Using Computational Techniques

Modeling and Optimization of COD Removal from Cold Meat Industry Wastewater by Electrocoagulation Using Computational Techniques

LightSit: An Unobtrusive Health-Promoting System for Relaxation and Fitness Microbreaks at Work

Modeling and optimization of a continuous electrocoagulation process using an artificial intelligence approach

Contact Info

Product

Resources

About