• Energy Research
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Abstract In this work, energy analysis is carried out to account for inputs and outputs of energy and GHG emissions associated with the biodiesel production system. Jojoba oil is obtained by cultivation of the jojoba plant, harvesting of seeds followed by extraction of oil, which is then converted to biodiesel by the process of transesterification. Energy efficiency is expressed in terms of the net energy balance (NEB) and the net energy ratio (NER). The use of the byproducts (husk, cake or meal, waste residues and glycerin) are also included as a part of the system boundary. The results show that the NEB and NER values are calculated at 46724.1 MJ/ha (28.9 MJ/L biodiesel produced) and 2.16, respectively. At the same time, the total amount of GHG emissions are estimated at 2.28 kg-CO2eq/L biodiesel produced (66.0 g CO2eq/MJ biodiesel produced). Nevertheless, if manure is used as fertilizer for jojoba plant cultivation, the primary energy input will fall by 9.52% (to 22.49 MJ/L of biodiesel).

Reduced scale experiments are often employed in engineering because they are much cheaper than real scale testing. Unfortunately, though, it is difficult to design a thermal-hydraulic circuit or equipment in reduced scale capable of reproducing, both accurately and simultaneously, all the physical phenomena that occur in real scale and operating conditions. This paper presents a methodology to designing thermal-hydraulic experiments in reduced scale based on setting up a constrained optimization problem that is solved using genetic algorithms (GAs). In order to demonstrate the application of the methodology proposed, we performed some investigations in the design of a heater aimed to simulate the transport of heat and momentum in the core of a pressurized water reactor (PWR) at 100% of nominal power and non-accident operating conditions. The results obtained show that the proposed methodology is a promising approach for designing reduced scale experiments.

Large amounts of CO2 from human socioeconomic activities threaten environmental sustainability. Moreover, uncontrolled resource use and lack of relevant technology exacerbate this issue. For this reason, carbon capture, utilization, and storage (CCUS) technology has gained worldwide attention. Many scholars have researched CCUS, but few have used CCUS patent bibliometric analysis from a unified perspective. This article aims to provide a conclusive analysis for CCUS researchers and policymakers, as well as summarize the innovation trends, technological distribution, and topic evolution. Based on 11,915 pieces of patent data from the Derwent Innovations Index, we used bibliometric analysis and data mining methods to conduct research on four dimensions: overall trend, geographical distribution, patentees, and patent content. The results of this article are as follows. CCUS has entered a rapid development stage since 2013. Patents are mainly distributed geographically in China, the US, and Japan, especially in heavy industries such as energy and electricity. Large enterprises hold patents with a relatively stable network of cooperators and attach great importance to international patent protection. A total of 12 topics were identified through clustering, and these topics gradually shifted from technicalities to commercialization, and from industrial production to all aspects of people’s daily lives.

AbstractClimate change poses new and unique challenges that threaten lives and livelihoods. Given the increasing risks and looming uncertainty of climate change, increasing attention has been directed towards adaptation, or the strategies that enable humanity to persist and thrive through climate change the best it can. Though climate change is a global problem often discussed at the national scale, urban areas are increasingly seen as having a distinct role, and distinctive motivation and capacity, for adaptation. The 12 articles in this special issue explore ways of understanding and addressing climate change impacts on urban areas. Together they reveal young but rapidly growing scholarship on how to measure, and then overcome, challenges of climate change. Two key themes emerge in this issue: 1) that we must identify and then overcome current barriers to urban adaptation and 2) frameworks/metrics are necessary to identify and track adaptation progress in urban settings. Both of these themes point to the power of indicators and other quantitative information to inform priorities and illuminate the pathway forward for adaptation. As climate change is an entirely new challenge, careful measurement that enables investment by private and public parties is necessary to provide efficient outcomes that benefit the greatest number of people.

Short-term load forecasting is an essential instrument in power system planning, operation and control. Many operating decisions are based on load forecasts, such as dispatch scheduling of generating capacity, reliability analysis, and maintenance planning for the generators. Overestimation of electricity demand will cause a conservative operation, which leads to the start-up of too many units or excessive energy purchase, thereby supplying an unnecessary level of reserve. On the contrary, underestimation may result in a risky operation, with insufficient preparation of spinning reserve, causing the system to operate in a vulnerable region to the disturbance. In this paper, semi-parametric additive models are proposed to estimate the relationships between demand and the driver variables. Specifically, the inputs for these models are calendar variables, lagged actual demand observations and historical and forecast temperature traces for one or more sites in the target power system. In addition to point forecasts, prediction intervals are also estimated using a modified bootstrap method suitable for the complex seasonality seen in electricity demand data. The proposed methodology has been used to forecast the half-hourly electricity demand for up to seven days ahead for power systems in the Australian National Electricity Market. The performance of the methodology is validated via out-of-sample experiments with real data from the power system, as well as through on-site implementation by the system operator.

This paper examines and optimizes parameters that affect the air cooling of a Lithium-Ion (Li-Ion) battery, used in Electric Vehicles (EVs). A battery pack containing 150 cylindrical type Li-Ion battery cells in a PVC casing is investigated. An equal number of tubes are used in the pack as a medium to cool the battery by using a fan when the vehicle is stationary or with ambient air when in motion. The parameters affecting the air cooling of battery are studied and optimized by considering their practical constraints. The objective function and Number of Transfer Unit (NTU) are developed. Finally, a genetic algorithm method is employed to optimize the decision variables. Analysing the results shows that NTU can be maximized by increasing the diameter of tubes on the battery and keeping the air velocity in a certain range.

AbstractBioenergy projects can lead to direct and indirect land use change (LUC), which can substantially affect greenhouse gas balances with both beneficial and adverse outcomes for bioenergy's contribution to climate change mitigation. The causes behind LUC are multiple, complex, interlinked, and change over time. This makes quantification uncertain and sensitive to many factors that can develop in different directions—including land use productivity, trade patterns, prices and elasticities, and use of by‐products associated with biofuels production. Quantifications reported so far vary substantially and do not support the ranking of bioenergy options with regard to LUC and associated emissions. There are however several options for mitigating these emissions, which can be implemented despite the uncertainties. Long‐rotation forest management is associated with carbon emissions and sequestration that are not in temporal balance with each other and this leads to mitigation trade‐offs between biomass extraction for energy use and the alternative to leave the biomass in the forest. Bioenergy's contribution to climate change mitigation needs to reflect a balance between near‐term targets and the long‐term objective to hold the increase in global temperature below 2°C (Copenhagen Accord). Although emissions from LUC can be significant in some circumstances, the reality of such emissions is not sufficient reason to exclude bioenergy from the list of worthwhile technologies for climate change mitigation. Policy measures to minimize the negative impacts of LUC should be based on a holistic perspective recognizing the multiple drivers and effects of LUC.This article is categorized under: Bioenergy > Economics and Policy Bioenergy > Climate and Environment

This paper investigates battery preheating before fast charging, for a battery electric vehicle (BEV) driving in a cold climate. To prevent the battery from performance degradation at low temperatures, a thermal management (TM) system has been considered, including a high-voltage coolant heater (HVCH) for the battery and cabin compartment heating. Accordingly, an optimal control problem (OCP) has been formulated in the form of a nonlinear program (NLP), aiming at minimising the total energy consumption of the battery. The main focus here is to develop a computationally efficient approach, mimicking the optimal preheating behavior without a noticeable increase in the total energy consumption. The proposed algorithm is simple enough to be implemented in a low-level electronic control unit of the vehicle, by eliminating the need for solving the full NLP in the cost of only 1Wh increase in the total energy consumption.

Abstract Flow-accelerated corrosion (FAC) is a degradation mechanism that affects carbon steel piping in power plants. The failures and degradation due to FAC have necessitated numerous replacements in many power plants. Several computer codes around the world were developed as part of a systematic program or process to control FAC in power plant utilities. The typical plant model requires the input of the flow parameters, piping configuration and the plant water chemistry. The results on FAC rate are considered the key to proper selection of components for inspection. The lack of information on the effect of the upstream components located in the proximity limited the accuracy of the FAC prediction tools and hence will affect the accuracy in identifying potential inspection locations. In the present study 211 inspection data for 90° carbon steel elbows from several nuclear power plants were used to determine the effect of the proximity between two components on the FAC wear rate. The effect of the velocity as well as the distance between the elbows and the upstream components is discussed in the present analysis. Based on the analyzed trends obtained from the inspection data, significant increase in the wear rate of approximately 70% on average is identified to be due to the proximity.