| 1. |
- El Gnaoui, Y., et al.
(författare)
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Biological pre-hydrolysis and thermal pretreatment applied for anaerobic digestion improvement : Kinetic study and statistical variable selection
- 2022
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Ingår i: Cleaner Waste Systems. - : Elsevier. - 2772-9125. ; 2
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Tidskriftsartikel (refereegranskat)abstract
- In the present study, two pretreatment methods (thermal pretreatment and biological pre-hydrolysis) were suggested for food waste (FW) with the aim to enhance biomass conversion and biogas production by anaerobic. The effects of thermal pretreatment (TP), including TP at 60°C and 80°C for 60 min, and TP at 100°C, 120°C and 140°C for 30 min, well as biological pre-hydrolysis (BPH) at 37°C, 55°C, 37°C followed by 55°C and 55°C followed by 37°C for 40 hour on anaerobic digestion performance of FW were evaluated in batch tests. Results were compared with untreated FW. The BPH and TP caused an increase in the soluble chemical oxygen demand and hydrolysis efficiency. The methane yield (MY) increased from 371.17 ml CH4/g VS for untreated FW to 471.95 ml CH4/ g VS. The maximal MY was recorded for BPH at 37°C for 20 h followed by 55°C for 20 h. The pretreatments increased the biogas production rate and reduced the lag phase. The most influential variables on the methane yield were investigated using three statistical methods: Principal component analysis, Mutual Information and R-squared. The results allowed a good modeling of the methane yield and minimized the overfitting effect. For reproduction and solid contribution to the field, we have attached to our article all the necessary material to reproduce the same statistical work as in the paper body.
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| 2. |
- Frimane, Âzeddine, 1990-, et al.
(författare)
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Infinite hidden Markov model for short-term solar irradiance forecasting
- 2022
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Ingår i: Solar Energy. - : Elsevier. - 0038-092X .- 1471-1257. ; 244, s. 331-342
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Tidskriftsartikel (refereegranskat)abstract
- Hidden state models are among the most widely used and efficient schemes for solar irradiance modeling in general and forecasting in particular. However, the complexity of such models – in terms of the number of states – is usually needed to be specified a priori. For solar irradiance data this assumption is very difficult to justify.In this paper, an infinite hidden Markov model (InfHMM) is introduced for short-term probabilistic forecasting of solar irradiance, where the assumption of fixed number of states a priori is relaxed and model complexity is determined during the model training. InfHMM is a non-parametric Bayesian model (NPB) indexed with an infinite dimensional parameter space which allows the automatic adaptation of the model to the “correct” complexity. This facilitates the automatic adaptation of the model to all weather conditions and locations. Posterior inference for InfHMM is performed using the Markov chain Monte Carlo algorithm, namely the beam sampler.Data from 13 different sources are used to validate the proposed model and subsequently it is compared to two well-established models in the literature: Markov-chain mixture distribution (MCM) and complete-history persistence ensemble (CH-PeEn) models. Important results are found, that cannot be derived from the existing finite models, such as the variation of the number of states within and across sites. The comparison of the models shows that the InfHMM is more consistent in term of the forecasting horizon.For reproducibility of the methodology presented in this paper, we have provided an R script for the InfHMM as supplementary material.
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| 3. |
- Ouhammou, Badr, et al.
(författare)
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Modeling of a bio-methane solar system driven by solar energy and heat pump
- 2022
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Ingår i: Biomass and Bioenergy. - : Elsevier. - 0961-9534 .- 1873-2909. ; 158
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Tidskriftsartikel (refereegranskat)abstract
- The paper presents a novel thermodynamic modeling approach of a pilot solar biomass system for bio-methane energy production and wastewater treatment by creation of two packages (STSB and MHPB) modeling platform under TRNsys studio software. Indeed, several newly developed mathematical schemes using the communicating elements: a new Multi-stage air to water Heat Pump, a new Vacuum Tube solar Collector (VTC), thermodynamic storage, and a biogas reactor) have been validated by experiment. The work presents and discusses two scenarios (packages), the first one –STSB- concerns the pilot solar-biomass system, and uses only renewable energy for the bio-methane production by anaerobic digestion mechanism under the mesophilic conditions (±35 °C to ±42 °C), The second scenario consists of a modeling Multi-stage Heat Pump Biomass reactor (MHPB). The packages developed have been tested for industrial case study. A validation process of two scenarios with experimental tests of a 500 L reactor -for the biomass of the cardboard paper-proves a good agreement, where the errors band is about 1% for STSB and 1.5% for the MHPB package. The biogas reactor treats about 900 (m³/year) liquid biomass (3 m³/day). The prediction results indicate that the biogas production attaint 51 (m³) per year. According to the biogas composition analysis, the bio-methane represents 74%, leading to prediction results of 38 (m³) per year of bio-methane for the case study. . Further, the extended results from the modeling packages platform indicate that for this installation type of 700 m³, the bio-methane (CH4) production is estimated of 52 836 (m³) per year, representing 64% of the total fuel consumption for the current case of cardboard recycling and packaging company.
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