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  • 1.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Andersson, Peter
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Andersson, Tim
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Tomas Aparicio, Elena
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Malardalen Univ, Future Energy Ctr, Sch Business Soc & Engn, SE-72123 Vasteras, Sweden.;Malarenergi AB, Sjohagsvagen 3, S-72103 Vasteras, Sweden..
    Baaz, Hampus
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Barua, Shaibal
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Bergström, Albert
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Bengtsson, Daniel
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Orisio, Daniele
    State Inst Higher Educ Guglielmo Marconi, Dalmine, Italy..
    Skvaril, Jan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zambrano, Jesus
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    A Machine Learning Approach for Biomass Characterization2019In: INNOVATIVE SOLUTIONS FOR ENERGY TRANSITIONS / [ed] Yan, J Yang, HX Li, H Chen, X, ELSEVIER SCIENCE BV , 2019, p. 1279-1287Conference paper (Refereed)
    Abstract [en]

    The aim of this work is to apply and evaluate different chemometric approaches employing several machine learning techniques in order to characterize the moisture content in biomass from data obtained by Near Infrared (NIR) spectroscopy. The approaches include three main parts: a) data pre-processing, b) wavelength selection and c) development of a regression model enabling moisture content measurement. Standard Normal Variate (SNV), Multiplicative Scatter Correction and Savitzky-Golay first (SGi) and second (SG2) derivatives and its combinations were applied for data pre-processing. Genetic algorithm (GA) and iterative PLS (iPLS) were used for wavelength selection. Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Regression (SVR) and traditional Partial Least Squares (PLS) regression, were employed as machine learning regression methods. Results shows that SNV combined with SG1 first derivative performs the best in data pre-processing. The GA is the most effective methods for variable selection and GPR achieved a high accuracy in regression modeling while having low demands on computation time. Overall, the machine learning techniques demonstrate a great potential to be used in future NIR spectroscopy applications.

  • 2.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Andersson, Peter
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Andersson, Tim
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Tomas Aparicio, Elena
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Mälarenergi AB, Sweden.
    Baaz, Hampus
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Barua, Shaibal
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. RISE SICS, Sweden.
    Bergström, Albert
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Bengtsson, Daniel
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Skvaril, Jan
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zambrano, Jesus
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Real-time Biomass Characterization in Energy Conversion Processes using Near Infrared Spectroscopy: A Machine Learning Approach2019In: “Innovative Solutions for Energy Transitions” / [ed] Elsevier, 2019, Vol. 158, p. 1279-1287Conference paper (Refereed)
    Abstract [en]

    The aim of this work is to apply and evaluate different chemometric approaches employing several machine learning techniques in order to characterize the moisture content in biomass from data obtained by Near Infrared (NIR) spectroscopy. The approaches include three main parts: a) data pre-processing, b) wavelength selection and c) development of a regression model enabling moisture content measurement. Standard Normal Variate (SNV), Multiplicative Scatter Correction and Savitzky-Golay first (SG1) and second (SG2) derivatives and its combinations were applied for data pre-processing. Genetic algorithm (GA) and iterative PLS (iPLS) were used for wavelength selection. Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Regression (SVR) and traditional Partial Least Squares (PLS) regression, were employed as machine learning regression methods. Results shows that SNV combined with SG1 first derivative performs the best in data pre-processing. The GA is the most effective methods for variable selection and GPR achieved a high accuracy in regression modeling while having low demands on computation time. Overall, the machine learning techniques demonstrate a great potential to be used in future NIR spectroscopy applications.

  • 3.
    Bürger, Raimund
    et al.
    Universidad de Concepción, Concepción, Chile.
    Careaga, Julio
    Lund University, Sweden.
    Diehl, Stefan
    Lund University, Sweden.
    Merckel, Ryan
    University of Pretoria, South Africa.
    Zambrano, Jesús
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Estimating the hindered-settling flux function from a batch test in a cone2018In: Chemical Engineering Science, ISSN 0009-2509, E-ISSN 1873-4405, Vol. 192, p. 244-253Article in journal (Refereed)
    Abstract [en]

    The hindered-settling velocity function for the modelling, simulation and control of secondary settling tanks can be determined from batch tests. The conventional method is to measure the velocity of the descending sludge-supernatant interface (sludge blanket) as the change in height over time in a vessel with constant cross-sectional area. Each such experiment provides one point on the flux curve since, under idealizing assumptions (monodisperse suspension, no wall-effects), the concentration of sludge remains constant just below the sludge blanket until some wave from the bottom reaches it. A newly developed method of estimation, based on the theory of nonlinear hyperbolic partial differential equations, is applied to both synthetic and experimental data. The method demonstrates that a substantial portion of the flux function may be estimated from a single batch test in a conical vessel. The new method takes into consideration that during an ideal settling experiment in a cone, the concentration just below the sludge blanket increases with time since the mass of suspended solids occupy a reduced volume over time.

  • 4.
    Diehl, Stefan
    et al.
    Lund Univ, Ctr Math Sci, POB 118,Lund, Sweden..
    Zambrano, Jesus
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Carlsson, Bengt
    Uppsala Univ, Dept Informat Technol, POB 337, Uppsala, Sweden..
    Analysis of photobioreactors in series2018In: Mathematical Biosciences, ISSN 0025-5564, E-ISSN 1879-3134, Vol. 306, p. 107-118Article in journal (Refereed)
    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.

  • 5.
    Diehl, Stefan
    et al.
    Lund University, Sweden.
    Zambrano, Jesús
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Carlsson, Bengt
    Uppsala University, Sweden.
    Steady-State Analyses of Activated Sludge Processes with Plug-Flow Reactor2017In: Journal of Environmental Chemical Engineering, ISSN 2160-6544, E-ISSN 2213-3437, Vol. 5, no 1, p. 795-809Article in journal (Refereed)
    Abstract [en]

    Activated sludge processes (ASPs) consisting of a plug-flow reactor (PFR) and a settler are analyzed in steady-state operation using a reduced model consisting of one soluble substrate and one particulate biomass component modelling the dominating biological process. Monod biomass growth rate is assumed. Two settler models are studied. One is the commonly used ideal settler, or point settler, which is assumed to never be overloaded and to have unlimited flux capacity. The other recently published steady-state settler model includes hindered and compressive settling, and models a realistic limiting flux capacity. Generally, the steady-state concentration profiles within the PFR and the settler are governed by nonlinear ordinary differential equations. It is shown that the steady-state behaviour of the ASP can, however, be captured by equations without derivatives. New theoretical results are given, such as conditions by means of inequalities on input variables and parameters for a steady-state solution to exist. Another novel finding is that, if the incoming substrate concentration is increased from a low or moderate stationary value and the solids residence time is kept fixed, then this results in a lower effluent concentration in the new steady state. The steady-state equations are solved numerically for different operating conditions. For common parameter values, numerical solutions reveal that an ASP having a PFR, instead of a continuously stirred tank reactor, is far more efficient in reducing the effluent substrate concentration and this can be obtained for much lower recycle ratios, which reduces the pumping energy considerably.

  • 6.
    Jonfelt, Clara
    et al.
    Uppsala University, Sweden.
    Zambrano, Jesús
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Lindblom, Erik
    Stockholm Vatten, Sweden.
    Nehrenheim, Emma
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Key parameters for modelling Anammox process with N2O emissions2017In: French Federation of Biotechnology - Bioreactors Symposium 2017: Innovative approaches in bioreactors design and operation, France, 2017Conference paper (Refereed)
    Abstract [en]

    In this paper, a sensitivity analysis and a calibration were applied to a recent published model used to replicate N2O emissions in an Anammox process of a moving-bed biofilm reactor (MBBR). The model used in this study was designed to replicate a one-stage nitrification-Anammox system in a MBBR at Hammarby-Sjöstad pilot plant (Stockholm, Sweden), whichtreats of anaerobic digestion liquor. The aeration was intermittently (45/15 minutes - on/off). During the aeration, a 1.5 mg/L DO set-point was set. Three main measurements wereobtained: NH4 in water, N2O in both water and gas phase.The sensitivity analysis was done via the one-at-a-time method, where one parameter at a timeis changed (in our case, 10%) from its nominal value and the model output is quantified. Next,the most sensitive parameters were used in the model calibration. Results indicate that the biofilm porosity (η [-]), biofilm density (ρ [gTS/m3]), maximum biofilmthickness (Lmax [mm]) and boundary layer thickness of the biofilm (L0 [μm]) were the mostsensitive parameters of the model. These parameters performed the model calibration.

  • 7.
    Kanders, Linda
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Purac AB, Sweden.
    Yang, Jing-jing
    IVL Swedish Environmental Research Institute, Sweden.
    Baresel, Christian
    IVL Swedish Environmental Research Institute, Sweden.
    Zambrano, Jesus
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Full-scale comparison of N2O emissions from SBR N/DN operation versus one-stage deammonification MBBR treating reject water: - and optimization with pHset-point2019In: Water Science and Technology, ISSN 0273-1223, E-ISSN 1996-9732, Vol. 79, no 8, p. 1616-1625Article in journal (Refereed)
    Abstract [en]

    To be able to fulfill the Paris agreement regarding anthropogenic greenhouse gases, all potential 12 emissions must be mitigated. Wastewater treatment plants should aim to eliminate emissions of the 13 most potent greenhouse gas, nitrous oxide. In this study, these emissions were measured at a full-scale 14 reject water treatment tank during two different operation modes: nitrification/denitrification (N/DN) 15 operating as a sequencing batch reactor (SBR), and deammonification (nitritation/anammox) as a moving 16 bed biofilm reactor (MBBR). Nitrous oxide was measured both in the water phase and in the off-gas. The 17 treatment process emitted significantly less nitrous oxide in deammonification mode 0.14-0.7 %, 18 compared to 10 % of Total Nitrogen in N/DN mode. The decrease can be linked to the change feeding 19 strategy, concentration in nitrite, load of ammonia oxidized, shorter aeration time, no ethanol dosage 20 and the introduction of biofilm. Further, evaluation was done how the operational pH set point 21 influenced the emissions in deammonification mode. Lower concentrations of nitrous oxide was 22 measured in water phase at higher pH (7.5-7.6) than at lower pH (6.6-7.1). This is believed to be mainly 23 because of the lower aeration ratio and increased complete denitrification at the higher pH set point.

  • 8.
    Lundström, Lukas
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Eskilstuna Kommunfastighet AB, Eskilstuna, Sweden.
    Akander, J.
    Division of Building, Energy and Environment Technology, Department of Technology and Environment, University of Gävle, Sweden.
    Zambrano, Jesus
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Development of a space heating model suitable for the automated model generation of existing multifamily buildings—a case study in Nordic climate2019In: Energies, ISSN 1996-1073, E-ISSN 1996-1073, Vol. 12, no 3, article id 485Article in journal (Refereed)
    Abstract [en]

    Building energy performance modeling is essential for energy planning, management, and efficiency. This paper presents a space heating model suitable for auto-generating baseline models of existing multifamily buildings. Required data and parameter input are kept within such a level of detail that baseline models can be auto-generated from, and calibrated by, publicly accessible data sources. The proposed modeling framework consists of a thermal network, a typical hydronic radiator heating system, a simulation procedure, and data handling procedures. The thermal network is a lumped and simplified version of the ISO 52016-1:2017 standard. The data handling consists of procedures to acquire and make use of satellite-based solar radiation data, meteorological reanalysis data (air temperature, ground temperature, wind, albedo, and thermal radiation), and pre-processing procedures of boundary conditions to account for impact from shading objects, window blinds, wind- and stack-driven air leakage, and variable exterior surface heat transfer coefficients. The proposed model was compared with simulations conducted with the detailed building energy simulation software IDA ICE. The results show that the proposed model is able to accurately reproduce hourly energy use for space heating, indoor temperature, and operative temperature patterns obtained from the IDA ICE simulations. Thus, the proposed model can be expected to be able to model space heating, provided by hydronic heating systems, of existing buildings to a similar degree of confidence as established simulation software. Compared to IDA ICE, the developed model required one-thousandth of computation time for a full-year simulation of building model consisting of a single thermal zone. The fast computation time enables the use of the developed model for computation time sensitive applications, such as Monte-Carlo-based calibration methods. 

  • 9.
    Nookuea, Worrada
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zambrano, Jesús
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Tan, Yuting
    Royal Institute of Technology, Sweden.
    Li, Hailong
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Thorin, Eva
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Yan, Jinyue
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Royal Institute of Technology, Stockholm, Sweden.
    Comparison of Mass Transfer Models on Rate-Based Simulations of CO2 Absorption and Desorption Processes2017In: Energy Procedia, ISSN 1876-6102, E-ISSN 1876-6102, Vol. 142, p. 3747-3752Article in journal (Refereed)
    Abstract [en]

    One of the keys available options for the large scale carbon capture and storage is the solvent-based post-combustion capture. Due to the high reactivity between CO2 and aqueous amine solutions, chemical absorption is suitable for capturing the CO2 at low concentration such as from the flue gas. From techno-economic analyses of the CO2 chemical absorption plant, absorber and desorber columns are the main cost of the purchased equipment. Since the process involves complex reactive separations, the accurate calculation of hydrodynamic properties, mass and energy transfer are of importance for the design of the columns. Several studies have been done on the impact of different process and property models on the equilibrium and rate-based simulation of the absorption site. However, the impact study of process and property models on the desorption site are still lacking. This paper performs rate-based simulations of CO2 absorption by Monoethanolamine. The software Aspen Plus was used for the simulations. Different mass transfer models were implemented for the mass transfer calculation in gas and liquid phases. The temperature and concentration profiles along the columns are reported and discussed.

  • 10.
    Olsson, Jesper
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Forkman, T.
    Swedish University of Agricultural Sciences, Sweden.
    Gentili, F.G.
    Swedish University of Agricultural Sciences, Sweden.
    Zambrano, Jesús
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Schwede, Sebastian
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Nehrenheim, Emma
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Thorin, Eva
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Anaerobic co-digestion of sludge and microalgae grown inmunicipal wastewater: A feasibility study2018In: Water Science and Technology, ISSN 0273-1223, E-ISSN 1996-9732, Vol. 77, no 3, p. 682-694Article in journal (Refereed)
    Abstract [en]

    In this study a natural mix of microalgae grown in wastewater of municipal character was co-digested with sewage sludge in mesophilic conditions, in both batch and semi-continuous modes. The semicontinuous experiment was divided into two periods with OLR 1 (Organic Loading Rate) of 2.4 kg VS m3 d-1 and HRT1 (Hydraulic Retention Time) of 15 days, and OLR2 of 3.5 kg VS m3 d-1 and HRT2 of 10 days respectively. Results showed stable conditions during both periods. The methane yield was reduced when adding microalgae (from 200 ± 25 NmL CH4 g VSin-1 , to 168±22 NmL CH4 g VSin-1). VS reduction was also decreased by 51%. This low digestability was confirmed in the anaerobic batch test. However, adding microalgae improved the dewaterability of the digested sludge. The high heavy metals content in the microalgae resulted in a high heavy metals content in the digestate, making it more difficult to reuse the digestate as fertilizer on arable land. The heavy metals are thought to originate from the flue gas used as a CO2 source during the microalgae cultivation. Therefore the implementation of CO2 mitigation via algal cultivation requires careful consideration regarding thesource of the CO2-rich gas.

  • 11.
    Pierong, Rasmus
    et al.
    Uppsala University.
    Nehrenheim, Emma
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Carlsson, Bengt
    Uppsala University.
    Zambrano, Jesús
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Algae Based Wastewater Treatment Model Using The RWQM12016Conference paper (Refereed)
    Abstract [en]

    In this paper, we propose a model describing the dynamics of an algae based wastewater treatment process in an activated sludge environment. As the basis for the process modelling, the River Water Quality Model no. 1 (RWQM1) is chosen. In order to evaluate the applicability of the model to an activated sludge process, the proposed model is compared to the Activated Sludge Model no. 1 (ASM1).

  • 12.
    Samuelsson, Oscar
    et al.
    Uppsala University, Sweden.
    Anders, Björk
    IVL Swedish Environmental Research Institute, Sweden.
    Zambrano, Jesús
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Carlsson, Bengt
    Uppsala University, Sweden.
    Gaussian process regression for monitoring and fault detection of wastewater treatment processes2017In: Water Science and Technology, ISSN 0273-1223, E-ISSN 1996-9732, Vol. 75, no 12, p. 2952-2963Article in journal (Refereed)
    Abstract [en]

    Monitoring and fault detection methods are increasingly important to achieve a robust and resource efficient operation of wastewater treatment plants (WWTPs). The purpose of this paper was to evaluate a promising machine learning method, Gaussian process regression (GPR), at WWTP monitoring applications. We evaluated GPR at two WWTP monitoring problems: estimate missing data in a flow rate signal (simulated data), and detect a drift in an ammonium sensor (real data). We showed that GPR with the standard estimation method, maximum likelihood estimation (GPR-MLE), suffered from local optima during estimation of kernel parameters, and did not give satisfactory results in a simulated case study. However, GPR with a state-of-the-art estimation method based on sequential Monte Carlo estimation (GPR-SMC) gave good predictions and did not suffer from local optima. Comparisons with simple standard methods revealed that GPR-SMC performed better than linear interpolation in estimating missing data in a noisy flow rate signal. We conclude that GPR-SMC is both a general and powerful method for monitoring full-scale WWTPs. However, this paper also shows that it does not always pay off to use more sophisticated methods. New methods should be critically compared against simpler methods, which might be good enough for some scenarios.

  • 13.
    Samuelsson, Oscar
    et al.
    Uppsala University, Sweden.
    Björk, Anders
    IVL Swedish Environmental Research Institute, Sweden..
    Zambrano, Jesus
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Carlsson, Bengt
    Uppsala University, Sweden.
    Fault signatures and bias progression in dissolved oxygen sensors2018In: Water Science and Technology, ISSN 0273-1223, E-ISSN 1996-9732, Vol. 78, no 5, p. 1034-1044Article in journal (Refereed)
    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.

  • 14.
    Samuelsson, Oscar
    et al.
    Uppsala University, Sweden.
    Björk, Anders
    IVL Swedish Environmental Research Institute, Sweden.
    Zambrano, Jesús
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Carlsson, Bengt
    Uppsala University, Sweden.
    Monitoring fouling on dissolved oxygen sensors in WRRFs with active fault detection2017Conference paper (Refereed)
    Abstract [en]

    Measurements of the dissolved oxygen (DO) concentration are central in aeration control strategies at Water Resource Recovery Facilities (WRRFs). Despite this, more research has been focused on DO-control strategies, see e.g. (Amand et al. 2013), than on fault detection (FD) methods to for DO-measurements. One FD-method was proposed in (Carlsson & Zambrano 2016), where the ratios between airflows at different aerated zones were monitored to detect bias in DO-sensors. However, this was argued to be inadequate to distinguish large process disturbances from sensor bias. It is a general problem that process disturbances are hard or impossible to separate from sensor faults.

    One approach that potentially could be used to distinguish between sensor and process fault is active fault detection, in contrast to traditional or passive fault detection. In active fault detection an auxiliary signal is designed exclusively for fault detection and injected into the system (Esna Ashari et al. 2012). In this paper, we used the impulse from an automatic air cleaning system of the DO-sensor as design signal, and monitored the impulse response, see Figure 1 for an example. A similar approach was suggested already in 1992 (Spanjers & Olsson 1992), where a changed time constant of the DO-sensor was shown to be a good indication of an artificially fouled DO-sensor. More recently, Andersson and Hallgren showed that the impulse response from an air-cleaning procedure could be used to detect a biologically fouled DO-sensor (Andersson S. & Hallgren F. 2015).

    However, none of the previous studies made repeated experiments of fouled versus cleaned sensors in order to characterize the variation between the impulse responses. This is needed to compare different FD-methods and their performance to distinguish fouled (faulty) from clean (normal) impulse responses.

    In this paper we made detailed experiments with artificial fouling and used the results to compare two fault detection methods, Rise time estimation (RTE), and Gaussian process regression (GPR) (Rasmussen & Williams 2005).

  • 15.
    Sylwan, Ida
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Nehrenheim, Emma
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Thorin, Eva
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zambrano, Jesús
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Removal of metals for improvement of sludge quality, adsorption to primary sludge during primary settlement2017Conference paper (Other academic)
    Abstract [en]

    The primary and secondary sludge from a wastewater treatment plant are generally mixed and treated combined. Here we introduce an idea for a process concept where the sludge flows are separated and the treatment of primary sludge is modified, with the goal to concentrate micropollutants in primary sludge while nutrients are removed in the secondary (biological) treatment to produce a “bio-sludge” with low metal contents. The example is based on primary settlement and an activated sludge process. In contrast to a conventional process, the sludge flows are as mentioned separated. After anaerobic digestion and dewatering, primary sludge goes through pyrolysis. Biochar produced during pyrolysis is added in pulverized or granulated form to the primary settler. The hypothesis is that biochar will adsorb dissolved metals and thus enhance the metal removal in primary treatment. The biochar should settle with primary sludge, and pyrolysis is repeated. However, to remove metal content from the system some portion of the produced biochar will have to be removed in each cycle. A prerequisite for nutrients to end up in the bio-sludge is that chemical coagulants are not used in primary treatment and that there is no recirculation of sludge from secondary to primary treatment. To the best of the authors knowledge, biochar has not previously been tested as an adsorbent in primary treatment of wastewater. Efficient removal of metals has though been shown in several studies where wastewater was filtrated through biochar in granulated form (Huggins et al., 2016). Further, biochar has been shown to sorb pharmaceuticals from urine without removing nutrients (Solanki & Boyer, 2017). In this paper, results from experimental tests on addition of biochar in the primary settler will be presented. Experiments are made in lab-scale to test the adsorption and settling capacity depending on biochar properties, e.g. particle size, cation exchange capacity. The theoretical dosing requirement in a full scale application and possible biochar yields from pyrolysis of primary sludge are also investigated.

  • 16.
    Sylwan, Ida
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Runtti, Hanna
    Oulu University, Finland.
    Thorin, Eva
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zambrano, Jesus
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Westholm, Lena Johansson
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    BIOCHAR ADSORPTION FOR SEPARATION OF HEAVY METALSIN MUNICIPAL WASTEWATER TREATMENT2018Conference paper (Other academic)
  • 17.
    Sylwan, Ida
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Zambrano, Jesus
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Thorin, Eva
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Energy demand for phosphorus recovery from municipal wastewater2019In: Innovative Solutions for Energy Transitions / [ed] Elsevier, 2019, Vol. 158, p. 4338-4343Conference paper (Refereed)
    Abstract [en]

    Phosphorus (P) is one of the essential nutrients for production of food. In modern agriculture, a large part of P comes from finite sources. There are several suggested processes for reuse of P from wastewater. In this paper, the energy use of direct reuse of sludge in agriculture is compared to the energy demand connected to use of mineral P and to reuse of P after thermal processing of sludge. The study is based on literature data from life cycle analysis (LCA). In the case of direct sludge reuse the sludge stabilization processes applied and the system boundaries of the LCA has a large impact on the calculated energy demand. The results though indicate that direct reuse of sludge in agriculture is the reuse scenario that potentially has the lowest energy demand (3-71 kWh/kg P), compared to incineration and extraction of P from sludge ashes (45-70 kWh/kg P) or pyrolysis of sludge (46-235 kWh/kg P). The competitiveness compared to mineral P (-4-22 kWh/kg P) depends on the mineral P source and production. For thermal processing, the energy demand derives mainly from energy needed to dry sludge and supplement fuel used during sludge incineration together with chemicals required to extract P. Local conditions, such as available waste heat for drying, can make one of these scenarios preferable.

  • 18.
    Zambrano, Jesus
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Samuelsson, Oscar
    IVL Swedish Environm Res Inst, Stockholm, Sweden.
    Carlsson, Bengt
    Uppsala Univ, Sweden.
    Machine learning techniques for monitoring the sludge profile in a secondary settler tank2019In: Applied water science, ISSN 2190-5487, E-ISSN 2190-5495, Vol. 9, no 6, article id UNSP 146Article in journal (Refereed)
    Abstract [en]

    The aim of this paper is to evaluate and compare the performance of two machine learning methods, Gaussian process regression (GPR) and Gaussian mixture models (GMMs), as two possible methods for monitoring the sludge profile in a secondary settler tank (SST). In GPR, the prediction of the response variable is given as a Gaussian probability density function, whereas in the GMM the probability density function is built as a weighted sum of Gaussian distributions. In both approaches, a residual is calculated and a fault detection criterion is implemented via a recursive decision rule. As case study, GMM and GPR were tested using real data from a sensor measuring the suspended solids concentration as a function of the SST level in a wastewater treatment plant in Bromma, Sweden. Results suggest that GMM gives a faster response but is also more sensitive than GPR to changes during normal conditions.

  • 19.
    Zambrano, Jesús
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Carlsson, Bengt
    Uppsala University, Sweden.
    Diehl, Stefan
    Lund University, Sweden.
    Nehrenheim, Emma
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    A Simplified Model of an Activated Sludge Process with a Plug-Flow Reactor2016Conference paper (Refereed)
    Abstract [en]

    The analysis of a simplified activated sludge process (ASP) with one main dissolved substrate and one main particulate biomass component has been conducted with respect to its steady-state. The ASP is formed by a plug flow reactor (PFR) and a settler with the recycling going to the reactor. The biomass growth rate is described by a Monod function. For this process, it is not possible to get an explicit expression for the effluent substrate concentration when the process is subject to a fixed sludge age. However,in the normal case when the influent substrate concentration is much greater than the effluent substrate concentration, then an explicit approximation for the effluent as a function of the influent and the process parameters is obtained. This work includes numerical examples considering two models for the settler. One model is the ideal settler, which assumes a complete thickening of the activated sludge through the underflow of the settler. The other model takes into account hindered settling and sludge compression. Numerical results show the effectiveness and the limitations of the proposed solution under these scenarios.

  • 20.
    Zambrano, Jesús
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Krustok, Ivo
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Nehrenheim, Emma
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Carlsson, Bengt
    Department of Information Technology, Uppsala University, Uppsala, Sweden.
    A simple model for algae-bacteria interaction in photo-bioreactors2016In: Algal Research, ISSN 2211-9264, Vol. 19, no nov, p. 155-161Article in journal (Refereed)
    Abstract [en]

    This work presents a simple model to describe the consortia of algae-bacteria in a photo-bioreactor. The model is inspired by the Activated Sludge Model (ASM) structure, which includes different process rates and stoichiometric parameters. The model comprises two main biomass populations (algae and bacteria), two dissolved substrates (ammonium and nitrate) and two dissolved gases (oxygen and carbon dioxide) in the reactor. The model was calibrated with data from batch experiments performed in two lab-scale photo-bioreactors. A sensitivity analysis was done to identify the parameters to be considered for the model calibration. Results indicate that the maximum algae and bacteria growth rate, bacteria growth yield and half-saturation constant for carbon were the most sensitive parameters. Moreover, the comparison between the experiments and the model shows good agreement in terms of predicting the ammonium, nitrate and oxygen concentrations in the photo-bioreactor.

  • 21.
    Zambrano, Jesús
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Nehrenheim, Emma
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Light and duty cycle optimization of a photo-bioreactor in batch mode2017In: Energy Procedia, ISSN 1876-6102, E-ISSN 1876-6102, Vol. 105, p. 773-779Article in journal (Refereed)
    Abstract [en]

    This paper is focused on optimizing the amount of light duty cycle of a photo-bioreactor operating in batch mode. The mathematical model used is confined to one dissolved substrate, one biomass (algae), one internal cell quota, and the irradiance for photo-acclimated culture. The model has been previously published and validated with experimental data. The following optimization problem is studied: minimize the effluent substrate concentration subject to: maximum and minimum amount of light to be used, the time of the light/dark illumination and the total time of the batch experiment. Analytical solution for this optimization problem seems difficult to obtain. However, numerical results obtained from simulations show that it is possible to find solutions which satisfy the problem requirements.

  • 22.
    Zambrano, Jesús
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Nehrenheim, Emma
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Microalgae Activated Sludge: Process Modelling and Optimization2017Conference paper (Refereed)
    Abstract [en]

    This work deals with steady-state optimization of a process formed by an algae-bacteria photo-bioreactor (PBR) in an activated sludge configuration. The optimization is done by considering the total PBR volume as two volumes in series, and aiming for the minimal nitrogen concentration in the effluent, for a given external light and carbon dioxide (CO2) injection. Results suggest that it is possible to obtain an optimum volume distribution that gives a lower effluent substrate concentration compared to a single volume, and this optimum volume depends on the CO2 applied.

  • 23.
    Zambrano, Jesús
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Samuelsson, Oscar
    Uppsala University.
    Carlsson, Bengt
    Uppsala University.
    Monitoring a Secondary Settler Using Gaussian Mixture Models2016Conference paper (Refereed)
    Abstract [en]

    This paper presents a method for monitoring the sludge profiles of a secondary settler using a Gaussian Mixture Model (GMM). A GMM is a parametric probability density function represented as a weighted sum of Gaussian components densities. To illustrate this method, the current approach is applied using real data from a sensor measuring the sludge concentration as a function of the settler level at a wastewater treatment plant (WWTP) in Bromma, Sweden. Results suggest that the GMM approach is a feasible methodfor monitoring and detecting possible disturbances of the process and fault situations such as sensor clogging. This approach can be a valuable tool for monitoring processes with a repetitive profile.

1 - 23 of 23
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