process simulation and dynamic control for marine oily
TRANSCRIPT
RESEARCH POSTER PRESENTATION DESIGN © 2015
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INTRODUCTION
OBJECTIVES
MATERIALS AND METHODS
RESULTS CONCLUSIONS
• This study examined the degradation kinetics of a typical PAH, namely naphthalene in seawater using UVC irradiation. Results show that fluence rate, temperature and the interaction between temperature and initial concentration are the most influential factors. An increase in fluence rate can linearly promote the photodegradation process. Salinity increasingly impedes the removal of naphthalene because of the existence of free-radical scavengers and photon competitors. A feed-forward back-propagation trained ANN was used to model this process.
• The proposed dynamic process control tool can well simulate and optimize nonlinear systems with good accuracy. Optimization results can be superior over traditional control strategies. This would help the application of advanced wastewater treatment technologies in terms of saving cost and time.
• The developed experimental-modeling approach and its concept/framework have high potential of applicability in other environmental fields where a treatment process is involved and experimentation and modeling are used for process simulation and control.
REFERENCES
• Jing, L., Chen, B., Zhang, B.Y., Peng, H.X., 2012a. A review of ballast water management practices and challenges in harsh and Arctic environments. Environmental Reviews, 20, 83–108.
• Jing, L., Chen, B., Zhang, B.Y., Li, P., 2012b. A stochastic simulation-based hybrid interval fuzzy programming approach for optimizing the treatment of recovered oily water. Journal of Ocean Technology, 7(4), 59–72.
• Jing, L., Chen, B., Zhang, B.Y., Zheng, J.S., Liu, B., 2014a. Naphthalene degradation in seawater by UV irradiation: the effects of fluence rate, salinity, temperature and initial concentration. Marine Pollution Bulletin, 81, 149-156.
• Jing, L., Chen, B., Zhang, B.Y., 2014b. Modeling of UV-induced photodegradation of naphthalene in marine oily wastewater by artificial neural networks. Water, Air, & Soil Pollution, 225(4), 1-14.
• Jing, L., Chen, B., Zhang, B.Y., Li, P., 2015. Process Simulation and Dynamic Control for Marine Oily Wastewater Treatment using UV Irradiation. Water Research, 81, 101-112.
ACKNOWLEDGEMENT Special thanks go to Natural Sciences and Engineering Research Council of Canada (NSERC), Canada Foundation for Innovation (CFI), Research & Development Corporation (RDC) of Newfoundland and Labrador, and Petroleum Research Newfoundland & Labrador (PRNL) for supporting this work.
• To investigate the removal of a typical PAH, Naphthalene, using UV irradiation in seawater (Jing et al., 2014a);
• To study and model the effects of photon flux, salinity and temperature using Artificial Neural Networks (ANN) (Jing et al., 2014b);
• To develop a simulation-based dynamic process control tool for marine oily wastewater treatment systems less cost, less time (Jing et al., 2015);
• To ensure a sound design and optimal operation in terms of cost, time and environmental standards.
Department of Civil Engineering Faculty of Engineering and Applied Science, Memorial University, St. John’s, NL, Canada, A1B 3X5 Liang Jing, Bing Chen*, Baiyu Zhang and Pu Li
Process Simulation and Dynamic Control for Marine Oily Wastewater Treatment using UV Irradiation
Marine Oily Wastewater Produced Water, Ballast Water, Bildge Water (Jing et al., 2012a)
70% Marine Oil Pollution
Polycyclic aromatic hydrocarbons (PAHs) are of
great concerns due to toxicity and persistence (Jing et al., 2012b)
Current gravity separation practices can not remove PAHs
Needs Polishing Treatment UV
Inner quartz jar & outer stainless steel chamber with UV lamps in between
When UV lamps ↑
Average UV intensity ↑
Input Layer
Hidden Layer
Output Layer
Photon Flux
Salinity
Temperature
Time
Naphthalene Removal Efficiency
Initial generation count Ng = 0Set Np, Ni, Pr, Pc, and Pm
Randomly generate initial string population (Np)
Run the ANN simulation model
Evaluate objective function and fitness values of the strings
Rank the strings and create a mating pool
Generate offspring population by reproduction and crossover
Perform mutation on the offspring population
Update generation countNg = Ng + 1
Is any convergence criterion met?
No
The top ranked string in the converged population
Yes
ANN
Process Control
Experimental
Experimental and ANN modeling results at (a) 10 and 500 μg L-1 initial concentrations with 40 oC, 32.5 ppt and 8.27 mW cm-2; (b) 25, 32.5, and 40 ppt
salinity with 20 oC, 10 μg L-1 and 2.88 mW cm-2; (c) 2.88 and 8.27 mW cm-2 fluence rate with 20 oC, 25 ppt and 10 μg L-1; and (d) 23 and 40 oC temperature with 40
ppt, 10 μg L-1 and 8.27 mW cm-2
• Factorial Design: two (or four, six, eight) UV lights; Temperature (20 and 40 oC); Salinity (0, 25, and 40 ppt). Total reaction time: 240 min.
Optimized treatment operation using the proposed dynamic process control – varying decision variables
Optimized treatment
operation using the traditional process
control – fixed decision variables
Minimization of treatment time and cost using both dynamic and
traditional process control CONTACT
This research water published in Water Research (IF = 5.528) in 2015. Contact: [email protected]; NRPOP Lab, Civil Engineering, Memorial