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SYNOPSIS In Swaziland policies to alleviate fuelwood shortages have been low on the list of development priorities, as much of the land has extensive bush and tree cover. There are, however, major social and geographical imbalances in biomass resource supply—some areas of Swazi Nation Land are barren due to increasing population pressure and the subsequent demand for fuelwood. This paper will show, in detail, the methodology for the survey which was carried out in Swaziland to investigate the importance of accessibility in linking potential supply to demand. Physical location, land tenure and management practices are generally recognised as important factors to take into consideration when converting potential resources into realistic amounts of fuelwood supply. Guesswork, however, is often used to estimate the impacts of these factors on biomass supplies. A nation-wide study was made in order to obtain more information on the accessibility of resources on privately owned land; results of a household surv...

Abstract Energy system modelling can be used to assess the implications of different scenarios and support improved policymaking. However, access to data is often a barrier to starting energy system modelling in developing countries, thereby causing delays. This article therefore provides data that can be used to create a simple zero order energy system model for Mauritania, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organizations, journal articles, and existing modelling studies. This means that the dataset can be easily updated based on the latest available information or more detailed and accurate local data. These data were also used to calibrate a simple energy system model using the Open Source Energy Modelling System (OSeMOSYS) and two stylized scenarios (Fossil Future and Least Cost ) for 2020-2050. The assumptions used and results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work.

Summary of modifications delivered to the shemaorg standard for energy M18

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Abstract Liquid fuels are likely to remain the main energy source in long-range transportation and aviation for several decades. To reduce our dependence on fossil fuels, liquid biofuels can be blended to fossil fuels – or used purely. In this paper, coconut methyl ester, standard diesel fuel (EN590:2017), and their blends were investigated in 25 V/V% steps. A novel turbulent combustion chamber was developed to facilitate combustion in a large volume that leads to ultra-low emissions. The combustion power of the swirl burner was 13.3 kW, and the air-to-fuel equivalence ratio was 1.25. Two parameters, combustion air preheating temperature and atomizing air pressure were adjusted in the range of 150–350 °C and 0.3–0.9 bar, respectively. Both straight and lifted flames were observed. The closed, atmospheric combustion chamber resulted in CO emission below 10 ppm in the majority of the cases. NO emission varied between 60 and 183 ppm at straight flame cases and decreased below 20 ppm when the flame was lifted since the combustion occurred in a large volume. This operation mode fulfills the 2015/2193/EU directive for gas combustion by 25%, which is twice as strict as liquid fuel combustion regulations. The 90% NO emission reduction was also concluded when compared to a lean premixed prevaporized burner under similar conditions. This favorable operation mode was named as Mixture Temperature-Controlled (MTC) Combustion. The chemiluminescent emission of lifted flames was also low, however, the OH* emission of straight flames was clearly observable and followed the trends of NO emission. The MTC mode may lead to significantly decreased pollutant emission of steady-operating devices like boilers, furnaces, and both aviation and industrial gas turbines, meaning an outstanding contribution to more environmentally friendly technologies.

AbstractThe bioenergy crops such as energycane, miscanthus, and sorghum are being genetically modified using state of the art synthetic biotechnology techniques to accumulate energy‐rich molecules such as triacylglycerides (TAGs) in their vegetative cells to enhance their utility for biofuel production. During the initial genetic developmental phase, many hundreds of transgenic phenotypes are produced. The efficiency of the production pipeline requires early and minimally destructive determination of oil content in individuals. Current screening methods require time‐intensive sample preparation and extraction with chemical solvents for each plant tissue. A rapid screen will also be needed for developing industrial extraction as these crops become available. In the present study, we have devised a proton relaxation nuclear magnetic resonance (1H‐NMR) method for single‐step, non‐invasive, and chemical‐free characterization of in‐situ lipids in untreated and pretreated lignocellulosic biomass. The systematic evaluation of NMR relaxation time distribution provided insight into the proton environment associated with the lipids in the biomass. It resolved two distinct lipid‐associated subpopulations of proton nuclei that characterize total in‐situ lipids into bound and free oil based on their “molecular tumbling” rate. The T1T2 correlation spectra also facilitated the resolution of the influence of various pretreatment procedures on the chemical composition of molecular and local 1H population in each sample. Furthermore, we show that hydrothermally pretreated biomass is suitable for direct NMR analysis unlike dilute acid and alkaline pretreated biomass which needs an additional step for neutralization.

Pervasive computing and business process modeling are increasingly joining forces, as mobile human users shall be seamlessly integrated into business processes. In respective scenarios, humans use mobile devices and wireless technology to interact with workflows running in a powerful back-end infrastructure. However, the frequent interaction between humans and workflows causes a high communication overhead and, thus, high energy consumption on mobile devices. This impacts the usability and efficiency of the business process due to rapidly drained batteries and the resulting short life-times of the devices and applications. We present an approach based on a minimum-cut algorithm for reducing costly data transmissions during workflow execution by distributing parts of a workflow to the users' devices. Our motivation is to reduce the energy consumption on the mobile devices and, thus, avoid draining batteries in the field. We prove that our algorithm finds the optimal solution for a given network and workflow, decreasing the energy consumed on mobile devices by 32-37% compared to an approach where the entire workflow is executed in the infrastructure. Thus, in typical domains like logistics and health care, one third of the energy can be saved. This either means that devices have to be charged less frequently, leading to less distraction in the business process, or that mobile device specifications can be lowered. Significant cost reductions result in both cases.

Even with state of the art forecasting methods, the short-term generation of wind farms cannot be predicted with a high degree of accuracy. In a market situation, these forecasting errors lead to commercial risk through imbalance costs when advance contracting. This situation is one that needs to be addressed due to the steady increase in the amount of grid connected wind generation, combined with the rise of deregulated, market orientated electricity systems. In the presence of imbalance prices and uncertain generation, a method is required to determine the optimum level of contract energy to be sold on the advance markets. Such a method is presented here using Markov probabilities for a wind farm and demonstrates substantial reductions in the imbalance costs. The effect of market closure delays and forecasting window lengths are also shown.