| 1. |
- Ahmed, Mobyen Uddin, Dr, 1976-, et al.
(författare)
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A machine learning approach for biomass characterization
- 2019
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Ingår i: Energy Procedia. - : Elsevier Ltd. - 1876-6102. ; , s. 1279-1287
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Konferensbidrag (refereegranskat)abstract
- 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. © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 - The 10th International Conference on Applied Energy.
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| 2. |
- Kanders, Linda, 1979-, et al.
(författare)
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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
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Ingår i: Water Science and Technology. - : IWA Publishing. - 0273-1223 .- 1996-9732. ; 79:8, s. 1616-1625
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 3. |
- Lundström, Lukas, 1980-, et al.
(författare)
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Development of a space heating model suitable for the automated model generation of existing multifamily buildings : a case study in Nordic climate
- 2019
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Ingår i: Energies. - : MDPI. - 1996-1073. ; 12:3
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 4. |
- Samuelsson, Oscar, et al.
(författare)
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Automated active fault detection in fouled dissolved oxygen sensors
- 2019
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Ingår i: Water Research. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0043-1354 .- 1879-2448. ; 166
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 5. |
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| 6. |
- Sylwan, Ida, et al.
(författare)
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Energy demand for phosphorus recovery from municipal wastewater
- 2019
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Ingår i: Energy Procedia. - : Elsevier Ltd. - 1876-6102. ; , s. 4338-4343, s. 4338-4343
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 7. |
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