Sequential treatment of paper and pulp industrial wastewater: Prediction of water quality parameters by Mamdani Fuzzy Logic model and phytotoxicity assessment

Mazhar, Sadat; Ditta, Allah; Bulgariu, Laura; Ahmad, Iftikhar; Ahmed, Munir; Nadiri, Ata Allah

doi:10.1016/j.chemosphere.2019.04.022

Cited by 88 publications

(34 citation statements)

References 37 publications

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“…pH potentially influenced the coagulation efficiency from the industrial effluents [ 10 ]. The influences of pH on BOD, COD, turbidity, and SS removal from PORE using tannin as a polymeric coagulant were evaluated, as present in Figure 2 .…”

Section: Resultsmentioning

confidence: 99%

“…The biological ponding process normally requires pre-treatment to remove residual oil and to minimize the concentration of the organic pollutant by dissolved air flotation (DAF) and coagulation using alum as a coagulant, respectively [ 6 , 8 , 9 ]. However, the biological treatment is a lengthy process, which requires prolonged treatment time for the degradation of organic particles [ 10 ]. Additionally, a lack of regulatory control and release of greenhouse gases from the biological treatment process are the major drawbacks of the existing PORE treatment process.…”

Section: Introductionmentioning

confidence: 99%

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Treatment of Palm Oil Refinery Effluent Using Tannin as a Polymeric Coagulant: Isotherm, Kinetics, and Thermodynamics Analyses

et al. 2020

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The refining of the crude palm oil (CPO) generates the palm oil refinery effluent (PORE). The presence of high contents of biochemical oxygen demand (BOD), chemical oxygen demand (COD), turbidity, and suspended solids (SS) in PORE encourages the determination of an effective treatment process to minimize the environmental pollution and preserve aquatic life. In the present study, a biodegradable natural polymer, namely tannin, was utilized as a coagulant to treat PORE. The coagulation experiment was conducted using a jar test apparatus. The tannin coagulation efficiency was evaluated based on the BOD, COD, turbidity, and SS removal from PORE by varying the tannin dose (50–300 mg/L), pH (pH 4–10), treatment time (15–90 min), and sedimentation time (15–90 min). It was found that the maximum removal of BOD, COD, turbidity, and SS was 97.62%, 88.89%, 93.01%, and 90.21%, respectively, at pH 6, a tannin dose of 200 mg/L, 60 min of coagulation time, and 60 min of sedimentation time. Analyses of isotherm models revealed that the Freundlich isotherm model was well fitted with the coagulation study. Kinetics studies show that the pseudo-second-order kinetics model was the well-fitted kinetics model for the BOD, COD, turbidity, and SS removal from PORE using tannin as a polymeric coagulant. The determination of thermodynamics parameters analyses revealed that BOD, COD, turbidity, and SS removal from PORE was spontaneous, exothermic, and chemical in nature. The finding of the present study shows that tannin as a natural polymeric coagulant would be utilized in PORE treatment to avoid toxic sludge generation.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Treatment of Palm Oil Refinery Effluent Using Tannin as a Polymeric Coagulant: Isotherm, Kinetics, and Thermodynamics Analyses

et al. 2020

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“…Two fuzzy inference systems namely Mamdani FIS [42] and Takagi-Sugeno FIS [42] can be used for a variety of control and prediction applications. However, Mamdani FIS is the most commonly adopted system by many researchers [48][49][50] for a range of applications than the Takagi-Sugeno FIS because of its ease of composition and interpretation [51]. Hence, Mamdani FIS has been employed in the present study.…”

Section: Fuzzy Inference Enginementioning

confidence: 99%

Prediction of Biogas Production in Upflow Anaerobic Sludge Blanket Reactor Based on Fuzzy Rule

Christian¹

2020

IJSDP

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The UASB reactor is a popular but complex anaerobic treatment mostly used to treat wastewater loaded with high organic matter. Hence, it is subjected to many complexities due to inconsistent quality and quantity of wastewater and therefore lots of uncertainties are incorporated. Thus, a fuzzy model was developed incorporating five input parameters to predict biogas production in UASBR treating distillery wastewater. A parametric sensitivity analysis of a fuzzy model has been carried out to effectively analyze the influence of input parameters on output. Effect of input parameters on the output parameter has been analyzed through scatter plots. Also, importance based ranking of various input parameters was conducted with the help of the sensitivity index. It was discovered that an increase in biogas production would be achieved if the temperature, COD reduction, COD load and alkalinity to acidity ratio is maintained as 35℃-42℃, 60,000-70,000 mg/L, 55,000-65,000 Kg/Day and 0.1-5 respectively. Moreover, the sensitivity indices of various parameters revealed that COD load and COD reduction had more importance on predicting biogas production. The results of the present study allow gaining important insights into key parameters which are responsible for affecting the performance of the UASBR under various input conditions.

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“…In 2018, Sadat Mazhar et al [5], worked on pulp and paper industrial wastewater. It was treated with three biological treatments through aerobic, sequential, and anaerobic that was 20 days of anaerobic goes after by 20 days of the aerobic cycle, related with imitation modeling by the MFL model of a few chosen parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

Convolutional Neural Network for Water Quality Prediction in WSN

2019

JNACS

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By the motivation of applicability of sensor nodes in various appliances like military target tracking, wildlife monitoring, and surveillance, and natural disaster relief, hazardous environment exploration, the incessant monitoring of water quality and features can be too an important technology to scrutinize the physicochemical parameters for increasing the succumbs. For that reason, a diversity of sensors can be positioned in the ponds to gather the need parameters and water quality detection can be performed by exploiting the data classification methods. Here, the Convolutional Neural Network (CNN) is exploited to predict the water quality of in Wireless Sensor Network (WSN). Initially, from the pond, the wireless Sensor Nodes (SN) is exploited to sense the data and CNN is modeled to select the output of the layer. Moreover, exploiting the Firefly with Dual update Process (FF-DUP) is used to choose the routing path in an optimal manner. After that, the outputs of Cluster Head (CH)-based routing protocols are transmitted to the sink node, in that the developed CNN is exploited to classify the water quality parameter. At last, the networking performance of the proposed method is evaluated by exploiting normalized energy utilization with the conventional models such as Low-energy adaptive clustering hierarchy based decision tree (LEACH+DT), LEACH based functional tangent decision tree (LEACH+FDT), fractional artificial bee colony algorithm based decision tree (FABC+DT).

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Sequential treatment of paper and pulp industrial wastewater: Prediction of water quality parameters by Mamdani Fuzzy Logic model and phytotoxicity assessment

Cited by 88 publications

References 37 publications

Treatment of Palm Oil Refinery Effluent Using Tannin as a Polymeric Coagulant: Isotherm, Kinetics, and Thermodynamics Analyses

Treatment of Palm Oil Refinery Effluent Using Tannin as a Polymeric Coagulant: Isotherm, Kinetics, and Thermodynamics Analyses

Prediction of Biogas Production in Upflow Anaerobic Sludge Blanket Reactor Based on Fuzzy Rule

Convolutional Neural Network for Water Quality Prediction in WSN

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