An Artificial Neural Network for Simulation of an Upflow Anaerobic Filter Wastewater Treatment Process
Abstract
:1. Introduction
- An efficient experimental plan that can be implemented in field conditions;
- The accuracy of the artificial neural network predictive model;
- The selection of mechanical constructive parameters based on significant differences in the performance results obtained by simulation.
2. Materials and Methods
2.1. Problem Solving Approach
- Installation of a bioreactor testing and data acquisition system at an industrial site;
- Preparation of an experimental plan to vary the operational parameters such that the test system is exposed to the full range of possible industrial conditions;
- Operation of the test system according to the experimental plan to acquire raw data;
- Restructuring and pre-treatment of the raw dataset according to the requirements of ANN models and to describe individual experiments according to the experimental plan;
- Construction of an artificial neural network model;
- Training and validation of the ANN model using supervised learning;
- Use of the ANN model to simulate test cases where mechanical parameters are varied within the range of tested values;
- Ranking of the simulation results in terms of the predicted performance of the upflow anaerobic filter bioreactor;
- Selection of the mechanical parameters according to the ranking of the simulation results.
2.2. Direct Experimental Data
2.3. Surrogate Data
2.4. Mock Experiments
- = a vector of calorific value reductions for a single mock experiment;
- = the influent flow rate on the ith day;
- = the change in the calorific value of the influent stream on the ith day of the reference experiment;
- = the spherical diameter effects on calorific value reduction on the ith day;
- = the material type effects on calorific value reduction on the ith day;
- = the height-to-diameter effects on calorific value reduction on the ith day.
2.5. Experiment Plan to Obtain the Mock Experimental Dataset
2.6. Data Preprocessing
2.7. Predictive Models
2.7.1. Polynomial Model
2.7.2. Multilayer Perceptron (MLP) Model
2.8. Use of the Model for Simulation
3. Results
3.1. Polynomial Model
3.2. MLP Model
3.3. Time-Series Effects
3.4. Model Selected for Use in Simulation
3.5. Evaluation of the Experimental Plan
3.6. Use of the MLP for Simulation
4. Discussion
4.1. Comparison to Existing Methods
4.2. Contribution of the Proposed Method
4.3. Future Implementation
5. Conclusions
- The predictions made using the MLP were more accurate than those made using the polynomial model (coefficients of determination (R), respectively, of 0.66 and 0.37);
- The MLP model used for simulation should be defined by architecture and not by a particular set of hidden-layer unit weight initializations;
- The reloaded MLP model can be used for simulation using previously unseen input vectors;
- The differences in the predictions are significant (p-value < 0.02, range = mean ± 20% of the full range);
- The ranked order of simulation results can be used to select the set of mechanical specifications that will result in the highest equipment performance;
- A practical on-site data collection plan can be used to reduce the resource requirements for new process development.
Supplementary Materials
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- IWA. International Water Association (IWA); IWA: London, UK, 2020. [Google Scholar]
- Dochain, D.; Vanrolleghem, P.A. Dynamical Modelling & Estimation in Wastewater Treatment Processes; IWA Publishing: London, UK, 2001; p. 9781780403045. [Google Scholar] [CrossRef]
- Barton, P.I.; Lee, C.K. Modeling, simulation, sensitivity analysis, and optimization of hybrid systems. ACM Trans. Model. Comput. Simul. 2002, 12, 256–289. [Google Scholar] [CrossRef]
- Dochain, D. (Ed.) Bioprocess Control; ISTE: London, UK, 2008; 248p. [Google Scholar]
- Ramaswamy, S.; Cutright, T.; Qammar, H. Control of a continuous bioreactor using model predictive control. Process Biochem. 2005, 40, 2763–2770. [Google Scholar] [CrossRef]
- Lima, F.V.; Rawlings, J.B. Nonlinear stochastic modeling to improve state estimation in process monitoring and control. AIChE J. 2011, 57, 996–1007. [Google Scholar] [CrossRef]
- Liotta, F.; Chatellier, P.; Esposito, G.; Fabbricino, M.; Van hullebusch, E.D.; Lens, P.N.L.; Pirozzi, F. Current Views on Hydrodynamic Models of Nonideal Flow Anaerobic Reactors. Crit. Rev. Environ. Sci. Technol. 2015, 45, 2175–2207. [Google Scholar] [CrossRef] [Green Version]
- Teixeira, A.P.; Alves, C.; Alves, P.M.; Carrondo, M.J.T.; Oliveira, R. Hybrid elementary flux analysis/nonparametric modeling: Application for bioprocess control. BMC Bioinform. 2007, 8, 30. [Google Scholar] [CrossRef] [Green Version]
- Zhang, Q.; Hu, J.; Lee, D.J.; Chang, Y.; Lee, Y.J. Sludge treatment: Current research trends. Bioresour. Technol. 2017, 243, 1159–1172. [Google Scholar] [CrossRef]
- Haimi, H.; Mulas, M.; Corona, F.; Vahala, R. Data-derived soft-sensors for biological wastewater treatment plants: An overview. Environ. Model. Softw. 2013, 47, 88–107. [Google Scholar] [CrossRef]
- Fisher, O.J.; Watson, N.J.; Porcu, L.; Bacon, D.; Rigley, M.; Gomes, R.L. Multiple target data-driven models to enable sustainable process manufacturing: An industrial bioprocess case study. J. Clean. Prod. 2021, 296, 126242. [Google Scholar] [CrossRef]
- Tetko, I.; Villa, A. An Enhancement of Generalization Ability in Cascade Correlation Algorithm by Avoidance of Overfitting/Overtraining Problem. Neural Process Lett. 1997, 6, 43–50. [Google Scholar] [CrossRef]
- Tetko, I.V.; Aksenova, T.I.; Volkovich, V.V.; Kasheva, T.N.; Filipov, D.V.; Welsh, W.J.; Livingstone, D.J.; Villa, A.E. Polynomial neural network for linear and non-linear model selection in quantitative-structure activity relationship studies on the internet. SAR QSAR Environ. Res. 2000, 11, 263–280. [Google Scholar] [CrossRef]
- Tetko, I.V.; Tanchuk, V.Y.; Kasheva, T.N.; Villa, A.E.P. Estimation of aqueous solubility of chemical compounds using E-state indices. J. Chem. Inf. Comput. Sci. 2001, 41, 1488–1493. [Google Scholar] [CrossRef] [PubMed]
- Harada, L.H.P.; da Costa, A.C.; Maciel Filho, R. Hybrid neural modeling of bioprocesses using functional link networks. Appl. Biochem. Biotechnol. 2002, 98–100, 1009–1023. [Google Scholar] [CrossRef]
- Ławryńczuk, M. Modelling and nonlinear predictive control of a yeast fermentation biochemical reactor using neural networks. Chem. Eng. J. 2008, 145, 290–307. [Google Scholar] [CrossRef]
- Rene, E.R.; López, M.E.; Kim, J.H.; Park, H.S. Back propagation neural network model for predicting the performance of immobilized cell biofilters handling gas-phase hydrogen sulphide and ammonia. BioMed Res. Int. 2013, 2013, 463401. [Google Scholar] [CrossRef]
- Sharghi, E.; Nourani, V.; Ashrafi, A.; Gökçekuş, H. Monitoring effluent quality of wastewater treatment plant by clustering based artificial neural network method. Pol. J. Environ. Stud. 2019, 164, 86–97. [Google Scholar] [CrossRef]
- Delnavaz, M.; Farahbakhsh, J.; Talaiekhozani, A.; Mehdinezhad Nouri, K. Predicting Removal Efficiency of Formaldehyde from Synthetic Contaminated Air in Biotrickling Filter Using Artificial Neural Network Modeling. J. Environ. Eng. 2019, 145, 04019056. [Google Scholar] [CrossRef]
- de Menezes, P.L.; de Azevedo, C.A.V.; Eyng, E.; Neto, J.D.; de Lima, V.L.A. Artificial neural network model for simulationof water distribution in sprinkle irrigation. Rev. Bras. Eng. Agrícola E Ambient. 2015, 19, 817–822. [Google Scholar] [CrossRef] [Green Version]
- Venkatesh Prabhu, M.; Karthikeyan, R. Comparative studies on modelling and optimization of hydrodynamic parameters on inverse fluidized bed reactor using ANN-GA and RSM. Alex. Eng. J. 2018, 57, 3019–3032. [Google Scholar] [CrossRef]
- Salehi, E.; Askari, M.; Aliee, M.H.; Goodarzi, M.; Mohammadi, M. Data-based modeling and optimization of a hybrid column-adsorption/depth-filtration process using a combined intelligent approach. J. Clean. Prod. 2019, 236, 117664. [Google Scholar] [CrossRef]
- Arismendy, L.; Cárdenas, C.; Gómez, D.; Maturana, A.; Mejía, R.; Quintero, M.C.G. Intelligent System for the Predictive Analysis of an Industrial Wastewater Treatment Process. Sustainability 2020, 12, 6348. [Google Scholar] [CrossRef]
- Witherow, J.L.; Coulter, J.B.; Ettinger, M.B. Anaerobic Contact Process for Treatment of Suburban Sewage. J. Sanit. Eng. Div. 1958, 88, 1–13. [Google Scholar] [CrossRef]
- Richard, R.; Dague, R.E.M.; Pfeffer, J.T. Anaerobic Activated Sludge. J. Water Pollut. Control Fed. 1966, 38, 220–226. [Google Scholar]
- Young, J.C.; McCarty, P.L. The anaerobic filter for waste treatment. J. Water Pollut. Control Fed. 1969, 41, 160. [Google Scholar]
- Genung, R.K.; Million, D.L.; Hancher, C.W.; Pitt, W.W., Jr. Pilot Plant Demonstration of an Anaerobic Fixed-Film Bioreactor for Wastewater Treatment; Oak Ridge National Lab.: Oak Ridge, TN, USA, 1978.
- Manariotis, I.D.; Grigoropoulos, S.G. Municipal-Wastewater Treatment Using Upflow-Anaerobic Filters. Water Environ. Res. 2006, 78, 233–242. [Google Scholar] [CrossRef]
- Córdoba, P.R.; Riera, F.S.; Sineriz, F. Temperature effects on upflow anaerobic filter performance. Environ. Technol. Lett. 1988, 9, 769–774. [Google Scholar] [CrossRef]
- Young, J.C. Factors Affecting the Design and Performance of Upflow Anaerobic Filters. Water Sci. Technol. 1991, 24, 133–155. [Google Scholar] [CrossRef]
- Bhattacharya, J.; Dev, S.; Das, B. Design of Wastewater Bioremediation Plant and Systems. In Low Cost Wastewater Bioremediation Technology; Butterworth-Heinemann: Oxford, UK, 2018; pp. 265–313. [Google Scholar]
- Zhu, G.Y.; Zamamiri, A.; Henson, M.A.; Hjortsø, M.A. Model predictive control of continuous yeast bioreactors using cell population balance models. Chem. Eng. Sci. 2000, 55, 6155–6167. [Google Scholar] [CrossRef]
- Tarjányi-Szikora, S.; Oláh, J.; Makó, M.; Palkó, G.; Barkács, K.; Záray, G. Comparison of different granular solids as biofilm carriers. Microchem. J. 2013, 107, 101–107. [Google Scholar] [CrossRef]
- Kacker, R.N.; Lagergren, E.S.; Filliben, J.J. Taguchi’s Orthogonal Arrays Are Classical Designs of Experiments. J. Res. Natl. Inst. Stand. Technol. 1991, 96, 577–591. [Google Scholar] [CrossRef]
- Roy, R.K. A Primer on the Taguchi Method, 2nd ed.; Society of Manufacturing Engineers: Dearborn, MI, USA, 2010. [Google Scholar]
- Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 2011, 12, 2825–2830. [Google Scholar]
- Hunter, J.D. Matplotlib: A 2D Graphics Environment. Comput. Sci. Eng. 2007, 9, 90–95. [Google Scholar] [CrossRef]
- McKinney, W. Data Structures for Statistical Computing in Python. In Proceedings of the Python in Science Conference, Austin, TX, USA, 28 June–3 July 2010; van der Walt, S., Millman, J., Eds.; SciPy: Austin, TX, USA, 2010; pp. 56–61. [Google Scholar] [CrossRef] [Green Version]
- Virtanen, P.; Gommers, R.; Oliphant, T.E.; Haberland, M.; Reddy, T.; Cournapeau, D.; Burovski, E.; Peterson, P.; Weckesser, W.; Bright, J.; et al. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nat. Methods 2020, 17, 261–272. [Google Scholar] [CrossRef] [Green Version]
- Iain Pardoe, L.S.; Young, D. STAT 462 Applied Regression Analysis. 2021. Available online: https://online.stat.psu.edu/stat462/node/180/ (accessed on 24 May 2022).
- Hinton, G.; Srivastava, N.; Swersky, K. Neural Networks for Machine Learning Lecture 6a Overview of mini- --Batch gradient descent. Cited 2012, 14, 2. [Google Scholar]
- NIST. NIST/SEMATECH e-Handbook of Statistical Methods, Section 7.2.2.2. 2021. Available online: https://www.itl.nist.gov/div898/handbook/prc/section2/prc222.htm (accessed on 14 March 2022).
- Koncsos, T. Bioreactor Simulation with Quadratic Neural Network Model Approximations and Cost Optimization with Markov Decision Process. Period. Polytech. Civ. Eng. 2020, 64, 614–622. [Google Scholar] [CrossRef]
- Antwi, P.; Li, J.; Meng, J.; Deng, K.; Quashie, F.K.; Li, J.; Boadi, P.O. Feedforward neural network model estimating pollutant removal process within mesophilic upflow anaerobic sludge blanket bioreactor treating industrial starch processing wastewater. Bioresour. Technol. 2018, 257, 102–112. [Google Scholar] [CrossRef] [Green Version]
- Sakiewicz, P.; Piotrowski, K.; Ober, J.; Karwot, J. Innovative artificial neural network approach for integrated biogas—Wastewater treatment system modelling: Effect of plant operating parameters on process intensification. Renew. Sustain. Energy Rev. 2020, 124, 109784. [Google Scholar] [CrossRef]
- Bolte, J.P.; Nordstedt, R.A.; Thomas, M.V. Mathematical Simulation of Upflow Anaerobic Fixed Bed Reactors. Trans. ASAE 1984, 27, 1483–1490. [Google Scholar] [CrossRef]
- Trösch, W.; Kiefer, W.; Lohmann, K.; Dürolf, H. Porous Inorganic Support Spheres Which Can Be Cleaned of Surface Biomass under Fluidized Bed Conditions. U.S. Patent 4,987,068, 22 January 1991. [Google Scholar]
- Joo-Hwa, T.; Kuan-Yeow, S.; Jeyaseelan, S. Media Factors Affecting the Performance of Upflow Anaerobic Packed-Bed Reactors. Environ. Monit. Assess. 1997, 44, 249–261. [Google Scholar] [CrossRef]
- Berardino, S.D.; Costa, S.; Converti, A. Semi-continuous anaerobic digestion of a food industry wastewater in an anaerobic filter. Bioresour. Technol. 2000, 71, 261–266. [Google Scholar] [CrossRef]
- Yilmaz, T. Modeling the performance of upflow anaerobic filter (UAF) reactors treating paper-mill wastewater using neural networks. Sci. Res. Essays 2013, 8, 1896–1905. [Google Scholar]
- Demir, S. Artificial Neural Network Simulation of Advanced Biological Wastewater Treatment Plant Performance. Sigma J. Eng. Nat. Sci. 2020, 38, 1713–1728. [Google Scholar]
- Hwangbo, S.; Al, R.; Chen, X.; Sin, G. Integrated Model for Understanding N2O Emissions from Wastewater Treatment Plants: A Deep Learning Approach. Environ. Sci. Technol. 2021, 55, 2143–2151. [Google Scholar] [CrossRef]
- Dibaba, O.R.; Lahiri, S.K.; T’Jonck, S.; Dutta, A. Experimental and Artificial Neural Network Modeling of a Upflow Anaerobic Contactor (UAC) for Biogas Production from Vinasse. Int. J. Chem. React. Eng. 2016, 14, 1241–1254. [Google Scholar] [CrossRef]
- Thomson, N.C.; Greenwald, K.; Lee, K.; Manso, G.F. The Computational Limits of Deep Learning. arXiv 2020, arXiv:2007.05558. [Google Scholar]
- Pisa, I.; Santín, I.; Vicario, J.; Morell, A.; Vilanova, R. ANN-Based Soft Sensor to Predict Effluent Violations in Wastewater Treatment Plants. Sensors 2019, 19, 1280. [Google Scholar] [CrossRef] [Green Version]
- Araujo, P.; Astray, G.; Ferrerio-Lage, J.A.; Mejuto, J.C.; Rodriguez-Suarez, J.A.; Soto, B. Multilayer perceptron neural network for flow prediction. J. Environ. Monit. 2011, 13, 35–41. [Google Scholar] [CrossRef]
- Hochreiter, S.; Schmidhuber, J. Long Short-Term Memory. Neural Comput. 1997, 9, 1735–1780. [Google Scholar] [CrossRef]
- Smith, L.C.; Elliot, D.J.; James, A. Mixing in upflow anaerobic filters and its influence on performance and scale-up. Water Res. 1996, 30, 3061–3073. [Google Scholar] [CrossRef]
- Olah, C. Understanding LSTMs. 2015. Available online: https://colah.github.io/posts/2015-08-Understanding-LSTMs/ (accessed on 14 March 2022).
- Escudié, R.; Conte, T.; Steyer, J.P.; Delgenès, J.P. Hydrodynamic and biokinetic models of an anaerobicfixed-bed reactor. Process Biochem. 2005, 40, 2311–2323. [Google Scholar] [CrossRef]
- Seabold, S.; Perktold, J. Statsmodels: Econometric and statistical modeling with python. In Proceedings of the 9th Python in Science Conference, Austin, TX, USA, 28 June–3 July 2010. [Google Scholar]
- Thirumurti, D. Effects of mixing velocity on anaerobic fixed film reactors. Water Res. 1988, 22, 517–523. [Google Scholar] [CrossRef]
Experiment Number | ESD | MAT | HDR | CVdt | Rank |
---|---|---|---|---|---|
E1 | 4 | PVC | 0.5 | 48.5 (46.4 ± 22.9) | 9 |
E2 (reference experiment) | 12 | TWD | 1.8 | 71.4 (75.9 ± 30.4) | 4 |
E3 | 36 | PUF | 3.6 | 79.6 (80.2 ± 50.4) | 2 |
E4 | 4 | TWD | 3.6 | 60.9 (61.8 ± 16.9) | 6 |
E5 | 12 | PUF | 0.5 | 43.8 (49.7 ± 27.4) | 8 |
E6 | 36 | PVC | 1.8 | 92.7 (91.0 ± 35.5) | 1 |
E7 | 4 | PUF | 1.8 | 51.3 (54.0 ± 19.3) | 7 |
E8 | 12 | PVC | 3.6 | 62.2 (69.1 ± 18.6) | 5 |
E9 | 36 | TWD | 0.5 | 82.5 (79.4 ± 44.7) | 3 |
Type of Numerical Model | RMSE (%) | R2 | Slope |
---|---|---|---|
Fourth degree polynomial, true time series | 14.9 | 0.29 | 0.24 |
Fourth degree polynomial, shuffled time series | 14.0 | 0.37 | 0.35 |
4-layer MLP, true time series | 12.7 | 0.48 | 0.38 |
4-layer MLP, shuffled time series | 10.1 | 0.66 | 0.67 |
Experiment Combination Description | RMSE | R2 |
---|---|---|
Experimental plan as described above (9 experiments) | 0.101 | 0.66 |
Experimental plan with experiment E3 removed | 0.096 | 0.72 |
Experimental plan with 1 experiment removed at random | 0.156 | 0.13 |
Mechanical Predictors (Factors) | Tested Values (Levels) |
---|---|
Packing diameter—ESD [mm] | 4, 8, 12, 24, 36 |
Type of packing material—MAT | 1, 1.85, 2.7, 6.85, 11 |
Fixed bed H/D ratio—HDR | 0.5, 1.15, 1.8, 2.9, 4 |
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McCormick, M. An Artificial Neural Network for Simulation of an Upflow Anaerobic Filter Wastewater Treatment Process. Sustainability 2022, 14, 7959. https://doi.org/10.3390/su14137959
McCormick M. An Artificial Neural Network for Simulation of an Upflow Anaerobic Filter Wastewater Treatment Process. Sustainability. 2022; 14(13):7959. https://doi.org/10.3390/su14137959
Chicago/Turabian StyleMcCormick, Mark. 2022. "An Artificial Neural Network for Simulation of an Upflow Anaerobic Filter Wastewater Treatment Process" Sustainability 14, no. 13: 7959. https://doi.org/10.3390/su14137959