Biochar in residual ash from gasifier cookstoves

Biomass based cooking with open fire is still used by more than three billion people worldwide [1]. Open fires normally produce ash, which contains both pure ash and some unburned carbon or biochar. The possible agricultural advantages by biochar as a soil amendment, has increasingly been discussed the last decade [2]. There exist many different types of cookstoves suited for simultaneously cooking and biochar production. Top Lit Up Draft (TLUD) – Natural Draft stoves are tested in several countries but represent a risk for condensation of pyrolysis gases on the charcoal during the combustion phases. In the present study two different TLUD stoves were tested according to the laboratory-based Water Boiling Test (WBT) with two different fuels: wood chips of oak, Quercus petrea, and standard 6 mm wood pellets. Two of the main goals were to evaluate cooking efficiency and biochar production including suitability as a soil amendment. After the WBT, the combustion continued until the flames disappeared and the remaining biochar was then quenched, cooled and put into plastic bags and sealed for later analysis. The biochar yield and corresponding cooking efficiency are shown in figure 1. The biochar yield is lower for higher efficiencies and in general around 19-23 % based on the biomass input. The biochar was analyzed and some of the major findings are shown in table 1. PCB and heavy metal content were in all cases low. Please click Additional Files below to see the full abstract

Characterization of biochar from Peko Pe gasifier cookstoves

Biomass based cooking with open fire is still used by more than three billion people worldwide [1]. Open fires normally produce ash, which contain both pure ash and some unburned carbon. Cookstoves like the Peko Pe in figure 1 has the possibility to produce biochar, if the combustion is quenched before complete burnt-out. PEKO PE belongs to the TLUD-ND (Top Lit Up Draft – Natural Draft) category of clean burning stoves. The PEKO PE principle, shown in figure 1 was invented by Paal Wendelbo nearly 30 years ago in Norway. The principle has later been improved first by Wendelbo and after his death by different relief organizations too. Please click on the file below for full content of the abstract

Validation and application of a multiparameter model for the densification of biochar

Densification is generally addressed to improve both the mechanical properties and the energy density of biomass feedstock. For such reason, it can also be used for biochar. However, the investigation of larger scales production by smaller academic equipment is not straightforward. It is therefore relevant to have access to models to facilitate a reliable comparison between the two different scales. In 2011, Holm et al. provided and validated a multiparameter model for analyzing industrial wood pelleting by a lab-scale single pellet press1. In 2017, the model was further verified for torrefied wood pelleting by Puig-Arnavat et al2. The intention of the authors of the present study was to prove the suitability of this model for pyrolyzed wood. It may help to understand how the densification process can be optimized at larger scales, especially regarding the forces that act through the pelleting channel, which are often cause of damages. Theoretically the model links the pelleting pressure , that the pellets undergo when ejected out of the channel, to the compression ratio c = x / 2r (where x is the length of the channel and r is its radius) 1. This is described by the equation: Please click Additional Files below to see the full abstract

A study of densified biochar as carbon source in the silicon and ferrosilicon production

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Digital Twin framework for automated fault source detection and prediction for comfort performance evaluation of existing non-residential Norwegian buildings

Numerous buildings fall short of expectations regarding occupant satisfaction, sustainability, or energy efficiency. In this paper, the performance of buildings in terms of occupant comfort is evaluated using a probabilistic model based on Bayesian networks (BNs). The BN model is founded on an in-depth anal- ysis of satisfaction survey responses and a thorough study of building performance parameters. This study also presents a user-friendly visualization compatible with BIM to simplify data collecting in two case studies from Norway with data from 2019 to 2022. This paper proposes a novel Digital Twin approach for incorporating building information modeling (BIM) with real-time sensor data, occupants’ feedback, a probabilistic model of occupants’ comfort, and HVAC faults detection and prediction that may affect occupants’ comfort. New methods for using BIM as a visualization platform, as well as a pre- dictive maintenance method to detect and anticipate problems in the HVAC system, are also presented. These methods will help decision-makers improve the occupants’ comfort conditions in buildings. However, due to the intricate interaction between numerous equipment and the absence of data integra- tion among FM systems, CMMS, BMS, and BIM data are integrated in this paper into a framework utilizing ontology graphs to generalize the Digital Twin framework so it can be applied to many buildings. The results of this study can aid decision-makers in the facility management sector by offering insight into the aspects that influence occupant comfort, speeding up the process of identifying equipment malfunc- tions, and pointing toward possible solutions.Digital Twin framework for automated fault source detection and prediction for comfort performance evaluation of existing non-residential Norwegian buildingspublishedVersionPaid open acces

Improving building occupant comfort through a digital twin approach:A Bayesian network model and predictive maintenance method

This study introduces a Bayesian network model to evaluate the comfort levels of occupants of two non-residential Norwegian buildings based on data collected from satisfaction surveys and building performance parameters. A Digital Twin approach is proposed to integrate building information modeling (BIM) with real-time sensor data, occupant feedback, and a probabilistic model of occupant comfort to detect and predict HVAC issues that may impact comfort. The study also uses 200000 points as historical data of various sensors to understand the previous building systems’ behavior. The study also presents new methods for using BIM as a visualization platform and for predictive maintenance to identify and address problems in the HVAC system. For predictive maintenance, nine machine learning algorithms were evaluated using metrics such as ROC, accuracy, F1-score, precision, and recall, where Extreme Gradient Boosting (XGB) was the best algorithm for prediction. XGB is on average 2.5% more accurate than Multi-Layer Perceptron (MLP), and up to 5% more accurate than the other models. Random Forest is around 96% faster than XGBoost while being relatively easier to implement. The paper introduces a novel method that utilizes several standards to determine the remaining useful life of HVAC, leading to a potential increase in its lifetime by at least 10% and resulting in significant cost savings. The result shows that the most important factors that affect occupant comfort are poor air quality, lack of natural light, and uncomfortable temperature. To address the challenge of applying these methods to a wide range of buildings, the study proposes a framework using ontology graphs to integrate data from different systems, including FM, CMMS, BMS, and BIM. This study’s results provide insight into the factors that influence occupant comfort, help to expedite identifying equipment malfunctions and point towards potential solutions, leading to more sustainable and energy-efficient buildings.publishedVersio

Multiobjective optimization of building energy consumption and thermal comfort based on integrated BIM framework with machine learning-NSGA II

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A review of the Digital Twin technology for fault detection in buildings

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