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Ahmed, M. U., Andersson, P., Andersson, T., Aparicio, E. T., Baaz, H., Barua, S., . . . Zambrano, J. (2019). A Machine Learning Approach for Biomass Characterization. In: Yan, J Yang, HX Li, H Chen, X (Ed.), INNOVATIVE SOLUTIONS FOR ENERGY TRANSITIONS: . Paper presented at 10th International Conference on Applied Energy (ICAE), AUG 22-25, 2018, Hong Kong, HONG KONG (pp. 1279-1287). ELSEVIER SCIENCE BV
Open this publication in new window or tab >>A Machine Learning Approach for Biomass Characterization
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2019 (English)In: INNOVATIVE SOLUTIONS FOR ENERGY TRANSITIONS / [ed] Yan, J Yang, HX Li, H Chen, X, ELSEVIER SCIENCE BV , 2019, p. 1279-1287Conference paper, Published 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.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV, 2019
Series
Energy Procedia, ISSN 1876-6102 ; 158
Keywords
Artificial Neural Network, Chemometrics, Gaussian Process Regression, Near Infrared Spectroscopy, Multiplicative Scatter Correction, Standard Normal Variate, Support Vector Regression, Partial Least Squares, Savitzky-Golay derivatives
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-44835 (URN)10.1016/j.egypro.2019.01.316 (DOI)000471031701100 ()
Conference
10th International Conference on Applied Energy (ICAE), AUG 22-25, 2018, Hong Kong, HONG KONG
Available from: 2019-07-11 Created: 2019-07-11 Last updated: 2019-07-11Bibliographically approved
Lundström, L., Akander, J. & Zambrano, J. (2019). Development of a space heating model suitable for the automated model generation of existing multifamily buildings—a case study in Nordic climate. Energies, 12(3), Article ID 485.
Open this publication in new window or tab >>Development of a space heating model suitable for the automated model generation of existing multifamily buildings—a case study in Nordic climate
2019 (English)In: Energies, ISSN 1996-1073, E-ISSN 1996-1073, Vol. 12, no 3, article id 485Article in journal (Refereed) Published
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. 

Place, publisher, year, edition, pages
MDPI AG, 2019
Keywords
Energy performance modeling, Gray box, ISO 52016-1, Meteorological reanalysis data, Satellite-based solar radiation data, Atmospheric temperature, Buildings, Computer software, Data handling, Energy efficiency, Heating equipment, Hot water heating, Ice, Monte Carlo methods, Solar radiation, Space heating, Energy performance, Reanalysis, Solar radiation data, Climate models
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-42698 (URN)10.3390/en12030485 (DOI)000460666200153 ()2-s2.0-85060858444 (Scopus ID)
Available from: 2019-02-15 Created: 2019-02-15 Last updated: 2019-03-28Bibliographically approved
Sylwan, I., Zambrano, J. & Thorin, E. (2019). Energy demand for phosphorus recovery from municipal wastewater. In: Elsevier (Ed.), Innovative Solutions for Energy Transitions: . Paper presented at International Conference on Applied Energy, 2018 (pp. 4338-4343). , 158
Open this publication in new window or tab >>Energy demand for phosphorus recovery from municipal wastewater
2019 (English)In: Innovative Solutions for Energy Transitions / [ed] Elsevier, 2019, Vol. 158, p. 4338-4343Conference paper, Published 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.

Series
Energy Procedia
Keywords
incineration; combustion; pyrolysis; wastewater sludge; nutrient reuse
National Category
Environmental Biotechnology
Research subject
Biotechnology/Chemical Engineering
Identifiers
urn:nbn:se:mdh:diva-40395 (URN)10.1016/j.egypro.2019.01.787 (DOI)000471031704107 ()2-s2.0-85063882988 (Scopus ID)
Conference
International Conference on Applied Energy, 2018
Available from: 2018-08-21 Created: 2018-08-21 Last updated: 2019-07-11Bibliographically approved
Kanders, L., Yang, J.-j., Baresel, C. & Zambrano, J. (2019). Full-scale comparison of N2O emissions from SBR N/DN operation versus one-stage deammonification MBBR treating reject water: - and optimization with pHset-point. Paper presented at IWA Nutrient Removal and Recovery Conference18 - 21 November 2018, Brisbane, Australia. Water Science and Technology, 79(8), 1616-1625
Open this publication in new window or tab >>Full-scale comparison of N2O emissions from SBR N/DN operation versus one-stage deammonification MBBR treating reject water: - and optimization with pHset-point
2019 (English)In: Water Science and Technology, ISSN 0273-1223, E-ISSN 1996-9732, Vol. 79, no 8, p. 1616-1625Article in journal (Refereed) Published
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.

Keywords
biological nitrogen removal, nitrous oxide (N2O) emissions, deammonification, pH
National Category
Water Treatment
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-43256 (URN)10.2166/wst.2019.163 (DOI)31169520 (PubMedID)2-s2.0-85067434025 (Scopus ID)
Conference
IWA Nutrient Removal and Recovery Conference18 - 21 November 2018, Brisbane, Australia
Available from: 2019-04-26 Created: 2019-04-26 Last updated: 2019-07-11Bibliographically 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 UNSP 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 UNSP 146Article in journal (Refereed) Published
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.

National Category
Energy Systems
Identifiers
urn:nbn:se:mdh:diva-44975 (URN)10.1007/s13201-019-1018-5 (DOI)000476592500001 ()
Available from: 2019-08-08 Created: 2019-08-08 Last updated: 2019-08-08Bibliographically approved
Ahmed, M. U., Andersson, P., Andersson, T., Tomas Aparicio, E., Baaz, H., Barua, S., . . . Zambrano, J. (2019). Real-time Biomass Characterization in Energy Conversion Processes using Near Infrared Spectroscopy: A Machine Learning Approach. In: Elsevier (Ed.), “Innovative Solutions for Energy Transitions”: . Paper presented at International Conference on Applied Energy, 2018 (pp. 1279-1287). , 158
Open this publication in new window or tab >>Real-time Biomass Characterization in Energy Conversion Processes using Near Infrared Spectroscopy: A Machine Learning Approach
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2019 (English)In: “Innovative Solutions for Energy Transitions” / [ed] Elsevier, 2019, Vol. 158, p. 1279-1287Conference paper, Published 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.

Series
Energy Procedia, ISSN 1876-6102
Keywords
Artificial Neural Netwrok; Chemometrics; Gaussian Process Regression; Multiplicative Scatter Correction; Standard Normal Variate; Support Vector Regression; Partial Least Squares; Savitzky-Golay derivatives
National Category
Environmental Engineering Environmental Biotechnology Industrial Biotechnology
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-40396 (URN)2-s2.0-85063865772 (Scopus ID)
Conference
International Conference on Applied Energy, 2018
Projects
FUDIPO
Funder
EU, Horizon 2020, 723523
Available from: 2018-08-21 Created: 2018-08-21 Last updated: 2019-04-25Bibliographically approved
Olsson, J., Forkman, T., Gentili, F., Zambrano, J., Schwede, S., Nehrenheim, E. & Thorin, E. (2018). Anaerobic co-digestion of sludge and microalgae grown inmunicipal wastewater: A feasibility study. Water Science and Technology, 77(3), 682-694
Open this publication in new window or tab >>Anaerobic co-digestion of sludge and microalgae grown inmunicipal wastewater: A feasibility study
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2018 (English)In: Water Science and Technology, ISSN 0273-1223, E-ISSN 1996-9732, Vol. 77, no 3, p. 682-694Article in journal (Refereed) Published
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.

Keywords
Biogas, dewaterability, Gompertz model, mesophilic, semi-continuous study, waste activated sludge
National Category
Renewable Bioenergy Research Water Engineering
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-37381 (URN)10.2166/wst.2017.583 (DOI)000424765000013 ()29431713 (PubMedID)2-s2.0-85042218057 (Scopus ID)
Projects
MAASICA-projektet
Funder
Knowledge Foundation
Available from: 2017-12-04 Created: 2017-12-04 Last updated: 2019-06-18Bibliographically 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
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-42253 (URN)10.1016/j.mbs.2018.07.005 (DOI)000453496200010 ()30059663 (PubMedID)2-s2.0-85054021664 (Scopus ID)
Available from: 2019-01-03 Created: 2019-01-03 Last updated: 2019-01-04Bibliographically approved
Sylwan, I., Runtti, H., Thorin, E., Zambrano, J. & Westholm, L. J. (2018). BIOCHAR ADSORPTION FOR SEPARATION OF HEAVY METALSIN MUNICIPAL WASTEWATER TREATMENT. In: : . Paper presented at SMICE2018, Sludge management in a circular economy, Rome, May 23-25, 2018.
Open this publication in new window or tab >>BIOCHAR ADSORPTION FOR SEPARATION OF HEAVY METALSIN MUNICIPAL WASTEWATER TREATMENT
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2018 (English)Conference paper, Poster (with or without abstract) (Other academic)
Keywords
biochar, adsorption, municipal wastewater, sludge, heavy metals, Ni, Pb, adsorption isotherm, Langmuir, Freundlich, Redlich-Peterson
National Category
Water Engineering
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-39348 (URN)
Conference
SMICE2018, Sludge management in a circular economy, Rome, May 23-25, 2018
Projects
SMET - Separation of heavy metals in municipal wastewater treatment
Available from: 2018-05-30 Created: 2018-05-30 Last updated: 2018-05-31Bibliographically approved
Bürger, R., Careaga, J., Diehl, S., Merckel, R. & Zambrano, J. (2018). Estimating the hindered-settling flux function from a batch test in a cone. Chemical Engineering Science, 192, 244-253
Open this publication in new window or tab >>Estimating the hindered-settling flux function from a batch test in a cone
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2018 (English)In: Chemical Engineering Science, ISSN 0009-2509, E-ISSN 1873-4405, Vol. 192, p. 244-253Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Identification; Inverse problem; Partial differential equation; Sedimentation
National Category
Engineering and Technology Chemical Sciences
Research subject
Mathematics/Applied Mathematics; Biotechnology/Chemical Engineering
Identifiers
urn:nbn:se:mdh:diva-40268 (URN)10.1016/j.ces.2018.07.029 (DOI)000443999000022 ()2-s2.0-85050301217 (Scopus ID)
Available from: 2018-07-20 Created: 2018-07-20 Last updated: 2018-10-04Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-8034-4043

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