Online measurement of alkalinity in anaerobic co-digestion using linear regression method
Keywords:
anaerobic digestion, alkalinity, online measurement, model, linear regressionAbstract
Alkalinity is a reliable indicator of process stability in anaerobic digestion system. Total alkalinity (TA) and partial alkalinity (PA) are usually monitored offline as indicators for the status of anaerobic digestion process. In order to online monitor TA and PA, the linear regression method was used as estimator to predict alkalinity via software sensor method. Parameters, namely, pH, oxidation and reduction potential (ORP), and electrical conductivity (EC), were used as input variables. EC was the most significant parameter with TA and PA. Multiple linear regression (MLR) models and simple linear regression models with EC were constructed to predict TA and PA in anaerobic co-digestion system. On the basis of the evaluation of prediction accuracy, the applications of linear regression models were better for monitoring PA than TA. MLR models provided higher accuracy for alkalinity prediction than simple linear regression models. The two MLR models based on single-phase anaerobic digestion system were also feasible to predict TA in anaerobic co-digestion systems. However, the accuracy of these models should be improved by calibrating for broad applications of linear regression method in online alkalinity measurement. Keywords: anaerobic digestion, alkalinity, online measurement, model, linear regression DOI: 10.3965/j.ijabe.20171001.2701 Citation: Bai X, Li Z F, Wang X M, He X, Cheng S K, Bai X F, et al. Online measurement of alkalinity in anaerobic co-digestion using linear regression method. Int J Agric & Biol Eng, 2017; 10(1): 176–183.References
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[3] Palacios-Ruiz B. Regulation of volatile fatty acids and total alkalinity in anaerobic digesters. World Congress, 2008; 67(8): 13611–13616.
[4] Borja R, Rincón B, Raposo F, Dominguez J R, Millan F, Martin A. Mesophilic anaerobic digestion in a fluidised-bed reactor of wastewater from the production of protein isolates from chickpea flour. Process Biochemistry, 2004; 39(12): 1913–1921.
[5] Fernández N, Montalvo S, Borja R, Guerrero L, Sanchez E, Cortes I, et al. Performance evaluation of an anaerobic fluidized bed reactor with natural zeolite as support material when treating high-strength distillery wastewater. Renewable Energy, 2008; 33(11): 2458–2466.
[6] Jenkins S R, Zhang X. Measuring the usable carbonate alkalinity of operating anaerobic digesters. Research Journal of the Water Pollution Control Federation, 1991; 63(1): 28–34.
[7] Björnsson L. Intensification of the biogas process by improved process monitoring and biomass retention. Doctoral Technical Thesis. Lund: Lund University, 2000, 4. 124p.
[8] Jantsch T G, Mattiasson B. A simple spectrophotometric method based on pH-indicators for monitoring partial and total alkalinity in anaerobic processes. Environmental Technology, 2003; 24(9): 1061–1067.
[9] Jantsch T G, Mattiasson B. An automated spectrophotometric system for monitoring buffer capacity in anaerobic digestion processes. Water Research, 2004; 38(17): 3645–3650.
[10] Bouvier J C, Steyer J P, Delgenes J P. On-line titrimetric sensor for the control of VFA and/or alkalinity in anaerobic digestion processes treating industrial vinasses. Iwa VII Latin American Workshop & Symposium on Anaerobic Digestion, 2002.
[11] Ward A J, Hobbs P J, Holliman P J, Jones D L. Evaluation of near infrared spectroscopy and software sensor methods for determination of total alkalinity in anaerobic digesters. Bioresource Technology, 2011; 102(5): 4083–4090.
[12] Aceves-Lara C A, Latrille E, Conte T, Steyer J P. Online estimation of VFA, alkalinity and bicarbonate concentrations by electrical conductivity measurement during anaerobic fermentation. Water Science & Technology, 2012; 65(7): 1281–1289.
[13] Argyropoulos A. Soft sensor development and process control of anaerobic digestion. PhD thesis. Exeter: University of Exeter, 2013, 10. 281p.
[14] Simeonov I, Diop S, Kalchev B, Chorukova E, Christov N. Design of software sensors for unmeasurable variables of anaerobic digestion processes. Stephan Angeloff Institute of Microbiology Bulgarian Academy of Sciences, 2012.
[15] Alcaraz-González V, Harmand J, Rapaport A, Steyer J P, González-Alvarez V, Pelayo-Ortiz C. Software sensors for highly uncertain WWTPs: a new approach based on interval observers. Water Research, 2002; 36(10): 2515–2524.
[16] Madsen M, Holm-Nielsen J B, Esbensen K H. Monitoring of anaerobic digestion processes: A review perspective. Renewable & Sustainable Energy Reviews, 2011; 15(6): 3141–3155.
[17] Lahav O, Morgan B E. Titration methodologies for monitoring of anaerobic digestion in developing countries—a review. Journal of Chemical Technology & Biotechnology, 2004; 79(12): 1331–1341.
[18] Nghiem L D, Manassa P, Dawson M, Fitzgerald S K. Oxidation reduction potential as a parameter to regulate micro-oxygen injection into anaerobic digester for reducing hydrogen sulphide concentration in biogas. Bioresource Technology, 2014; 173(19): 443–447.
[19] Blanc F C, Molof A H. Electrode potential monitoring and electrolytic control in anaerobic digestion. Water Pollution Control Federation, 1973; 45(4): 655–667.
[20] Hawkes F R, Guwy A J, Hawkes D L, Rozzi A G. On-line monitoring of anaerobic digestion: Application of a device for continuous measurement of bicarbonate alkalinity. Water Science & Technology, 1994; 30(12): 1–10.
[21] APHA. Standard Methods for Examination of Water and Wastewater (22nd Ed.). Washington DC, USA: 2012.
[22] Ripley L E, Converse J C. Improved alkalimetric monitoring for anaerobic digestion of high-strength waste. Water Pollution Control Federation, 1986; 58(5): 406–411.
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Published
2017-01-23
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Xue, B., Zifu, L., Xuemei, W., Xi, H., Shikun, C., Xiaofeng, B., & Ruiling, G. (2017). Online measurement of alkalinity in anaerobic co-digestion using linear regression method. International Journal of Agricultural and Biological Engineering, 10(1), 176–183. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/2701
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Renewable Energy and Material Systems
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