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Carlsson, Bengt
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Publications (10 of 87) Show all publications
Samuelsson, O., Zambrano, J., Björk, A. & Carlsson, B. (2019). Automated active fault detection in fouled dissolved oxygen sensors. Water Research, 166, Article ID 115029.
Open this publication in new window or tab >>Automated active fault detection in fouled dissolved oxygen sensors
2019 (English)In: Water Research, ISSN 0043-1354, E-ISSN 1879-2448, Vol. 166, article id 115029Article in journal (Refereed) Published
Abstract [en]

Biofilm formation causes bias in dissolved oxygen (DO) sensors, which hamper their usage for automatic control and thereby balancing energy- and treatment efficiency. We analysed if a dataset that was generated with deliberate perturbations, can automatically be interpreted to detect bias caused by biofilm formation. We used a challenging set-up with realistic conditions that are required for a full-scale application. This included automated training (adapting to changing normal conditions) and automated tuning (setting an alarm threshold) to assure that the fault detection (FD)-methods are accessible to the operators. The results showed that automatic usage of FD-methods is difficult, especially in terms of automatic tuning of alarm thresholds when small training datasets only represent the normal conditions, i.e. clean sensors. Despite the challenging set-up, two FD-methods successfully improved the detection limit to 0.5 mg DO/L bias caused by biofilm formation. We showed that the studied dataset could be interpreted equally well by simpler FD-methods, as by advanced machine learning algorithms. This in turn indicates that the information contained in the actively generated data was more vital than its interpretation by advanced algorithms.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD, 2019
Keywords
Active fault detection, Monitoring, Receiver operating characteristics, Gaussian process regression, One-class classification
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-397583 (URN)10.1016/j.watres.2019.115029 (DOI)000493221600037 ()31541793 (PubMedID)
Available from: 2019-11-25 Created: 2019-11-25 Last updated: 2019-11-25Bibliographically approved
Chistiakova, T., Wigren, T. & Carlsson, B. (2019). Combined L2-stable feedback and feedforward aeration control in a wastewater treatment plant. IEEE Transactions on Control Systems Technology, 27
Open this publication in new window or tab >>Combined L2-stable feedback and feedforward aeration control in a wastewater treatment plant
2019 (English)In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 27Article in journal (Refereed) Epub ahead of print
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-374737 (URN)10.1109/TCST.2019.2891410 (DOI)
Available from: 2019-01-23 Created: 2019-01-23 Last updated: 2019-06-14Bibliographically approved
Liu, H., Yang, C., Carlsson, B., Qin, S. J. & Yoo, C. (2019). Dynamic nonlinear partial least squares modeling using Gaussian process regression. Industrial & Engineering Chemistry Research, 58(36), 16676-16686
Open this publication in new window or tab >>Dynamic nonlinear partial least squares modeling using Gaussian process regression
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2019 (English)In: Industrial & Engineering Chemistry Research, ISSN 0888-5885, E-ISSN 1520-5045, Vol. 58, no 36, p. 16676-16686Article in journal (Refereed) Published
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:uu:diva-395316 (URN)10.1021/acs.iecr.9b00701 (DOI)000486360700043 ()
Available from: 2019-08-07 Created: 2019-10-18 Last updated: 2019-10-23Bibliographically approved
Zambrano, J., Samuelsson, O. & Carlsson, B. (2019). Machine learning techniques for monitoring the sludge profile in a secondary settler tank. Applied water science, 9(6), Article ID 146.
Open this publication in new window or tab >>Machine learning techniques for monitoring the sludge profile in a secondary settler tank
2019 (English)In: Applied water science, ISSN 2190-5487, E-ISSN 2190-5495, Vol. 9, no 6, article id 146Article in journal (Refereed) Published
National Category
Water Treatment Control Engineering
Identifiers
urn:nbn:se:uu:diva-391296 (URN)10.1007/s13201-019-1018-5 (DOI)000476592500001 ()
Available from: 2019-07-20 Created: 2019-08-22 Last updated: 2019-08-30Bibliographically approved
Diehl, S., Zambrano, J. & Carlsson, B. (2018). Analysis of photobioreactors in series. Mathematical Biosciences, 306, 107-118
Open this publication in new window or tab >>Analysis of photobioreactors in series
2018 (English)In: Mathematical Biosciences, ISSN 0025-5564, E-ISSN 1879-3134, Vol. 306, p. 107-118Article in journal (Refereed) Published
Abstract [en]

A photobioreactor (PBR) contains microalgae which under illumination consume carbon dioxide and substrate dissolved in water, and produce oxygen. The process is used in water recovery resource facilities with a continuous flow of wastewaster through the PBR. With several PBRs in series the reduction of substrate can be improved. This paper contains a thorough analysis of a model of PBRs in series, where each PBR is modelled with a system of three ordinary differential equations for the concentrations of dissolved substrate and biomass (algae), and the internal cell quota of substrate to biomass. Each PBR has a certain volume and irradiation. The absorption rate of substrate into the cells is modelled with Monod kinetics, whereas the biomass growth rate is modelled with Droop kinetics, in which both a minimum and a maximum internal cell quota are assumed. The main result is that the model has a unique stable steady-state solution with algae in all PBRs. Another stable steady-state solution is the wash-out solution with no algae in the system. Other steady-state solutions are combinations of these two with no algae in some of the first PBRs and algae in the rest of the PBRs in the series. Conditions on the illumination, volumetric flow and volumes of the PBRs are given for the respective solution. Numerical solutions illustrate the theoretical results and indicate further properties.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE INC, 2018
Keywords
Bioreactors in series, Droop model, Irradiance, Microalgae, Modelling, Steady state
National Category
Water Treatment
Identifiers
urn:nbn:se:uu:diva-373006 (URN)10.1016/j.mbs.2018.07.005 (DOI)000453496200010 ()30059663 (PubMedID)
Available from: 2019-01-11 Created: 2019-01-11 Last updated: 2019-01-11Bibliographically approved
Samuelsson, O., Bjork, A., Zambrano, J. & Carlsson, B. (2018). Fault signatures and bias progression in dissolved oxygen sensors. Water Science and Technology, 78(5), 1034-1044
Open this publication in new window or tab >>Fault signatures and bias progression in dissolved oxygen sensors
2018 (English)In: Water Science and Technology, ISSN 0273-1223, E-ISSN 1996-9732, Vol. 78, no 5, p. 1034-1044Article in journal (Refereed) Published
Abstract [en]

Biofilm fouling is known to impact the data quality of sensors, but little is known about the exact effects. We studied the effects of artificial and real biofilm fouling on dissolved oxygen (DO) sensors in full-scale water resource recovery facilities, and how this can automatically be detected. Biofilm fouling resulted in different drift direction and bias magnitudes for optical (OPT) and electrochemical (MEC) DO sensors. The OPT-sensor was more affected by biofilm fouling compared to the MEC-sensor, especially during summer conditions. A bias of 1 mg/L was detected by analysing the impulse response (IR) of the automatic air cleaning system in the DO sensor. The IR is an effect of a temporal increase in DO concentration during the automatic air cleaning. The IRs received distinct pattern changes that were matched with faults including: biofilm fouling, disturbances in the air supply to the cleaning system, and damaged sensor membrane, which can be used for fault diagnosis. The results highlight the importance of a condition-based sensor maintenance schedule in contrast to fixed cleaning intervals. Further, the results stress the importance of understanding and detecting bias due to biofilm fouling, in order to maintain a robust and resource-efficient process control.

Keywords
biofilm fouling, condition-based maintenance, dissolved oxygen sensor, fault detection, wastewater
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:uu:diva-368999 (URN)10.2166/wst.2018.350 (DOI)000447742600005 ()30339528 (PubMedID)
Available from: 2018-12-14 Created: 2018-12-14 Last updated: 2018-12-14Bibliographically approved
Chistiakova, T., Wigren, T. & Carlsson, B. (2018). Input–output stability design of an ammonium based aeration controller for wastewater treatment. In: Proc. American Control Conference: ACC 2018. Paper presented at ACC 2018, June 27–29, Milwaukee, WI (pp. 2964-2971). American Automatic Control Council
Open this publication in new window or tab >>Input–output stability design of an ammonium based aeration controller for wastewater treatment
2018 (English)In: Proc. American Control Conference: ACC 2018, American Automatic Control Council , 2018, p. 2964-2971Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
American Automatic Control Council, 2018
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-349422 (URN)10.23919/ACC.2018.8431151 (DOI)978-1-5386-5428-6 (ISBN)
Conference
ACC 2018, June 27–29, Milwaukee, WI
Available from: 2018-08-16 Created: 2018-04-27 Last updated: 2018-08-27Bibliographically approved
Chistiakova, T., Mattsson, P., Carlsson, B. & Wigren, T. (2018). Nonlinear system identification of the dissolved oxygen to effluent ammonium dynamics in an activated sludge process.
Open this publication in new window or tab >>Nonlinear system identification of the dissolved oxygen to effluent ammonium dynamics in an activated sludge process
2018 (English)Report (Other academic)
Series
Technical report / Department of Information Technology, Uppsala University, ISSN 1404-3203 ; 2018-011
National Category
Control Engineering Water Treatment
Identifiers
urn:nbn:se:uu:diva-367049 (URN)
Available from: 2018-08-13 Created: 2018-11-27 Last updated: 2018-11-27Bibliographically approved
Samuelsson, O., Björk, A., Zambrano, J. & Carlsson, B. (2017). Gaussian process regression for monitoring and fault detection of wastewater treatment processes. Water Science and Technology, 75(12), 2952-2963
Open this publication in new window or tab >>Gaussian process regression for monitoring and fault detection of wastewater treatment processes
2017 (English)In: Water Science and Technology, ISSN 0273-1223, E-ISSN 1996-9732, Vol. 75, no 12, p. 2952-2963Article in journal (Refereed) Published
National Category
Control Engineering Water Treatment
Identifiers
urn:nbn:se:uu:diva-329772 (URN)10.2166/wst.2017.162 (DOI)000404557300024 ()28659535 (PubMedID)
Available from: 2017-06-30 Created: 2017-09-21 Last updated: 2017-10-11Bibliographically approved
Samuelsson, O., Björk, A., Zambrano, J. & Carlsson, B. (2017). Monitoring fouling on dissolved oxygen sensors in WRRFs with active fault detection. In: Proc. 12th IWA Specialized Coference on Instrumentation, Control and Automation: . Paper presented at ICA2017, June 11–14, Québec, Canada.
Open this publication in new window or tab >>Monitoring fouling on dissolved oxygen sensors in WRRFs with active fault detection
2017 (English)In: Proc. 12th IWA Specialized Coference on Instrumentation, Control and Automation, 2017Conference paper, Published paper (Refereed)
National Category
Control Engineering Water Treatment
Identifiers
urn:nbn:se:uu:diva-329774 (URN)
Conference
ICA2017, June 11–14, Québec, Canada
Available from: 2017-09-21 Created: 2017-09-21 Last updated: 2019-07-03Bibliographically approved
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