Modeling of UV-Induced Photodegradation of Naphthalene in Marine Oily Wastewater by Artificial Neural Networks

Jing, Liang; Chen, Bing; Zhang, Baiyu

doi:10.1007/s11270-014-1906-0

Cited by 39 publications

(15 citation statements)

References 45 publications

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“…Compared with RSM, ANN can be better used to study the effect of cultural parameters individually as well as their interactions with one another. In the present study, a sensitivity analysis has been performed with a view to determining the efficacy of a parameter by constructing an ANN model using the “Perturb” method . With the optimal ANN model with 20 neurons in the hidden layer, the performance of 3 variables—initial pH, inoculum size, and incubation period—was assessed.…”

Section: Resultsmentioning

confidence: 99%

Process‐centric and data‐centric strategies for enhanced production of l‐asparaginase—an anticancer enzyme, using marine‐derived Aspergillus niger

Vala

Dudhagara

Dave

2018

Journal of Chemometrics

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The objective of the study was to achieve enhanced production of l‐asparaginase (LA), an anticancer enzyme, by a marine‐derived Aspergillus niger isolate. To improve LA production, optimization of pH, incubation time, and inoculum size was performed using process‐centric (response surface methodology [RSM]) and data‐centric (artificial neural network [ANN]) approaches. The optimized conditions led to a 108.62% rise in LA production. Upon comparison of 2 models for enhanced LA production, based on the R2, mean absolute percentage error, root mean square error, and mean absolute deviation values, the ANN model was observed to be superior over the RSM model. To the authors' best knowledge, the present finding is the first ever report revealing detailed analyses for RSM and ANN models for LA production using a marine‐derived fungus.

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Section: Resultsmentioning

confidence: 99%

Process‐centric and data‐centric strategies for enhanced production of l‐asparaginase—an anticancer enzyme, using marine‐derived Aspergillus niger

Vala

Dudhagara

Dave

2018

Journal of Chemometrics

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“…Divide the simulation-based sub-functions fn(x) into multiple stages and obtain the corresponding minimized fn(x) using the ANN-DMINP approach (Jing et al, 2015). The ANN model(s) are developed according to experimental data prior to this problem solving process (Jing et al, 2014a, b).…”

Section: The Is-pcop Approach For Marine Wastewater Managementmentioning

confidence: 99%

“…The storage tanks were connected to an UV reaction tank (10 m 3 ) where the average UV fluence rate could be controlled at 2.88, 4.27, 5.65, 6.96, and 8.27 mW cm -2 , respectively. This 5-level UV setting was the same as what has been used in Jing et al (2014a, b), where different numbers of UV lamps (i.e., 2 ~ 6) were used to reflect different fluence rates. The treatment process could therefore be simulated using the ANN model developed by Jing et al, (2014a).…”

Section: Bilge Water Treatment Systemmentioning

confidence: 99%

“…This 5-level UV setting was the same as what has been used in Jing et al (2014a, b), where different numbers of UV lamps (i.e., 2 ~ 6) were used to reflect different fluence rates. The treatment process could therefore be simulated using the ANN model developed by Jing et al, (2014a). Water contained in storage and reaction tanks was assumed to be completely mixed.…”

Section: Bilge Water Treatment Systemmentioning

confidence: 99%

“…A large portion of marine oil pollution is caused by the operational discharge of oily wastewater, such as bilge water, offshore produced water, ballast water, and oil tank washing water (Jing et al, 2014a). Owing to the stringent environmental regulations, secondary polishing treatment systems, such as advanced oxidation processes (AOPs), have been advocated as a necessary supplement to the traditional gravity-based ones in terms of pollution reduction.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Integrated Simulation-based Process Control and Operation Planning (IS-PCOP) System for Marine Oily Wastewater Management

Jing¹,

Chen²,

Zhang³

et al. 2016

J ENVIRON INFORM

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ABSTRACT. An ideal combination of process control and operation planning can reduce system cost and maximize economic and environmental benefits. This research proposed an integrated simulation-based process control and operation planning (IS-PCOP) system by using neural networks, genetic algorithm, multistage principle, and Monte Carlo simulation. It could well link process control and operation planning by simultaneously adopting different time-scales in computation. The hourly process control strategy forwarded the results to the operation planning module where long-term arrangements could be further evaluated. The use of ANN modeling also played a key role in predicting the nonlinear behavior of complex processes. In addition, Monte Carlo simulation yielded a better insight on uncertainties, which may arise from a number of different sources. A case study on marine wastewater management was carried out to demonstrate the efficacy of the proposed approach. Six different treatment standards (i.e., 5, 10, 15, 20, 25, and 30 μg L −1 ) were examined over a 20-day period and the 20 μg L −1 standard appeared to be the most economic option with a mean net cost of $18 per day. As compared to the traditional operation planning without process control, the integrated approach achieved more economically competitive results. By addressing the uncertainties and expressing the results in probability distributions, the decision makers would have more confidence in making decisions on both short-and long-term operations. It was concluded that the combination of process control and operation planning could help meet the economic objectives and ensure timely completion of the tasks.

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Intelligent tools to model photocatalytic degradation of beta‐naphtol by titanium dioxide nanoparticles

Ijadpanah-Saravi

Safari

Noruzi-Masir³

et al. 2017

Journal of Chemometrics

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Feasibility of applying intelligent tools in prediction and optimization of photocatalytic degradation of beta-naphthol using the titanium dioxide (TiO 2 ) nanoparticles were conducted in this study. Biphasic TiO 2 nanoparticles were synthesized using the controlled hydrolysis of TiCl 4 , and their properties were studied using the Xray diffraction and transmission electron microscopy methods. Therefore, factors affecting photocatalytic degradation of beta-naphthol including impurity concentration, catalyst content, acidity, and aeration rate were monitored and controlled. The laboratory data showed that degradation rate of beta-naphthol is a complicated nonlinear function of monitored variables. Two models including artificial network trained with particle swarm optimization (ANN-PSO) and adaptive neuro-fuzzy interference system trained with particle swarm optimization (ANFIS-PSO) were used for prediction of this system. The results showed presence of a significant relation between the real and predicted data of these 2 models. However, ANFIS-PSO can be more efficiently applied for prediction and optimization of photocatalytic behavior of TiO 2 nanoparticles as for degradation of beta-naphthol as compared to ANN-PSO. As an advantage, ANFIS eliminates the problems of fuzzy logic, such as creation of membership functions, and local minima, which should be located in design of ANN, and through PSO algorithm, it could be a very powerful tool for simulating kinds of processes.

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Modeling of UV-Induced Photodegradation of Naphthalene in Marine Oily Wastewater by Artificial Neural Networks

Cited by 39 publications

References 45 publications

Process‐centric and data‐centric strategies for enhanced production of l‐asparaginase—an anticancer enzyme, using marine‐derived Aspergillus niger

Process‐centric and data‐centric strategies for enhanced production of l‐asparaginase—an anticancer enzyme, using marine‐derived Aspergillus niger

An Integrated Simulation-based Process Control and Operation Planning (IS-PCOP) System for Marine Oily Wastewater Management

Intelligent tools to model photocatalytic degradation of beta‐naphtol by titanium dioxide nanoparticles

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