Neural networks as a tool for control and management of a biological reactor for treating hydrogen sulphide

Bioprocess and Biosystems Engineering

Volumn 29, Issue 2, 2006, Pages 129-136

  Elias A ^a   Ibarra Berastegi, G ^a   Arias, R ^a   Barona, A ^a  

^a UNIVERSITY OF THE BASQUE COUNTRY UPV EHU   (Spain)

Author keywords

Artificial neural networks; Bioreactors; Hydrogen sulphide; Numerical analysis; Statistical modelling

Indexed keywords

BIOFILTERS; CONCENTRATION (PROCESS); DATABASE SYSTEMS; NEURAL NETWORKS; REACTION KINETICS;

HYDROGEN SULPHIDE; MULTILAYER PERCEPTRON 2-2-1; REMOVAL EFFICIENCY; STATISTICAL MODELING;

BIOREACTORS;

HYDROGEN SULFIDE;

AIR POLLUTANT; AIR POLLUTION CONTROL; AIRFLOW; ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOFILTER; BIOREACTOR; CONCENTRATION (PARAMETERS); DATA BASE; EQUIPMENT DESIGN; MATHEMATICAL COMPUTING; PERCEPTRON; PREDICTIVE VALIDITY; PRIORITY JOURNAL; PROCESS MONITORING; PROCESS TECHNOLOGY; STATISTICAL MODEL;

AIR POLLUTANTS; HYDROGEN SULFIDE; MODELS, BIOLOGICAL; NEURAL NETWORKS (COMPUTER); WASTE MANAGEMENT;

EID: 33745698111     PISSN: 16157591     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00449-006-0062-3     Document Type: Article

References (33)

1. van Groenestijn JW, Kraakman NJR (2005) Recent developments in biological waste gas purification in Europe. Chem Eng J 113(2-3):85-91
2. Deshusses MA, Johnson C (2000) Development and validation of a simple protocol to rapidly determine the performance of biofilters for VOC treatment. Environ Sci Technol 34:461-467
3. Chmiel K, Konieczny A, Palica M, Jarzebski AB (2005) Periodic operation of biofilters. A concise model and experimental validation. Chem Eng Sci 60:2845-2850
4. Morales M, Hernández S, Cornabé T, Revah S, Auria R (2003) Effect of drying on biofilter performance: modeling and experimental approach. Environ Sci Technol 37:985-992
5. Alonso C, Sudian MT, Sorial GA, Smith FL, Biswas P, Smith PJ, Brenner RC (1997) Gas treatment in trickle-bed biofilters: biomass, how much is enough? Biotechnol Bioeng 54(6):583-594
6. Alonso C, Suidan MT, Kim BR, Kim BJ (1998) Dynamic mathematical model for the biodegradation of VOCs in a biofilter: biomass accumulation study. Environ Sci Technol 31:3118-3123
7. Delhoménie MC, Bibeau L, Gendron J, Brzezinski R, Heitz M (2003) A study of clogging in a biofilter treating toluene vapors. Chem Eng J 94:211-222
8. Iliuta I, Larachi F (2004) Transient biofilter aerodynamics and clogging for VOC degradation. Chem Eng Sci 59:3293-3302
9. Zarook S, Shaikh AA, Ansar Z (1997) Development, experimental validation and dynamic analysis of a general transient biofilter model. Chem Eng Sci 52:759-773
10. Schwarz BCE, Devinny JS, Tsotsis TT (2001) A biofilter network model - importance of the pore structure and other large-scale heterogeneities. Chem Eng Sci 56:475-483
11. Gerrard AM, Misiaczek O, Hajkova D, Halecky M, Paca J (2005) Steady state models for the biofiltration of styrene/air mixtures. Chem Biochem Eng Q 19(2):185-190
12. Spigno G, Zilli M, Nicolella C (2004) Mathematical modelling and simulation of phenol degradation in biofilters. Biochem Eng J 19:267-275
13. Devinny JS, Ramesh J (2005) A phenomenological review of biofilter models. Chem Eng J 113:187-196
14. Elias A, Ibarra-Berastegi G, Ormazabal J, Murgia I, Zugazti P (1996) ADM: a model for water treatment in an anaerobic biological reactor. In: Development and application of computer techniques to environmental studies VI. III, CMP. WIT, Southampton, pp 153-162
15. Liu G, Mi Z (2004) Prediction of pulsation frequency of pulsing flow in trickle-bed reactors using artificial neural network. Chem Eng Sci 59:5787-5794
16. Steyer JP, Harmand J (2003) Fault detection and isolation in wastewater treatment plants: comparison of different approaches and experimental results. In: Agathos SN, Reineke W (eds) Biotechnology for the environment: wastewater treatment and modelling, waste gas handling, Kluwer Academic Publishers, Dordrecht, vol. 3C, pp 87-100
17. Strik DPBTB, Domnanovich AM, Zani L, Braun R, Holubar P (2005) Prediction of trace compounds in biogas from anaerobic digestion using the MATLAB Neural Network Toolbox. Environ Model Softw 20:803-810
18. Hodgson BJ, Taylor CN, Ushio M, Leigh JR, Kalganova T, Baganz F (2004) Intelligent modelling of bioprocesses: a comparison of structured and unstructured approaches. Bioprocess Biosyst Eng 26:353-359
19. Maier R, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Model Softw 15:101-124
20. Gardner MW, Dorling R (1998) Artificial neural networks (the multilayer perceptron). A review of applications in the atmospheric sciences. Atmos Environ 32:2627-2636
21. Masters T (1993) Practical neural network recipes in C + +. Academic, San Diego
22. Maier HR, Morgan N, Chow CWK (2004) Use of artificial neural networks for predicting optimal alum doses and treated water quality parameters. Environ Model Softw 19:485-494
23. Bowden G, Maier HR, Dandy GC (2001) Optimal division of data for neural network models in water resources applications. Water Resour Res 38:21-28
24. 2S and product analysis. Process Biochem 37:813-820
25. Barona A, Elías A, Arias R, Cano I, Gonzalez R (2004) Effect of sudden variations in operating conditions on biofilter performance. Biochem Eng J 22(1):25-31
26. Barona A, Elias A, Amurrio A, Cano I, Arias R (2005) Hydrogen sulphide adsorption on a waste material used in bioreactors. Biochem Eng J 24:79-86
27. Willmott CJ (1981) On the validation of models. Phys Geogr 2:184-194
28. Willmott CJ (1982) Some comments on the evaluation of model performance. Bull Am Meteorol Soc 63:1309-1313
29. Willmott CJ, Ackleson SG, Davis RE, Feddema JJ, Klink KM, Legates DR, O'Donnell J, Rowe MC (1985) Statistics for the evaluation and comparison of models. J Geophys Res 90:8995-9005
30. Hanna SR, Strimaitis DG, Chang JC (1991) User's guide for software for evaluating hazardous gas dispersion models. American Petroleum Institute, Washington
31. Comrie A (1997) Comparing neural networks and regression models for ozone forecasting. J Air Waste Manag Assoc 47:653-663
32. Hunter A, Kennedy L, Henry J, Ferguson RI (2000) Application of neural networks and sensitivity analysis to improved prediction of trauma survival. Comput Methods Algorithms Biomed 62:11-19
33. Wilks DS (1995) Statistical methods in the atmospheric sciences. Academic, San Diego