Journal of Energy Engineering

A kinetic model of coal gasification in a Texaco gasifier was developed using the software Aspen Plus, and the model was validated using experimental data from previously reported research. The effects of the gasification temperature, use of steam (${\mathrm{H}}_2\mathrm{O}$) or carbon dioxide (${\mathrm{CO}}_2$) as the gasification agent (GA), and the gasification agent/carbon molar ratio (GA/C) on the composition, net ${\mathrm{CO}}_2$ emissions, and ${\mathrm{H}}_2/\mathrm{CO}$ molar ratio of the resulting syngas were evaluated. The results showed that for both ${\mathrm{H}}_2\mathrm{O}$ and ${\mathrm{CO}}_2$, CO was preferentially generated at high temperatures, whereas ${\mathrm{H}}_2$ and ${\mathrm{CO}}_2$ were dominant at low temperatures, although the carbon conversion was higher and the net ${\mathrm{CO}}_2$ emissions lower at high temperatures. On the other hand, the concentration of ${\mathrm{H}}_2$ was higher when ${\mathrm{H}}_2\mathrm{O}$ was employed as the GA, reaching 49% when the ${\mathrm{H}}_2\mathrm{O}/\mathrm{C}$ ratio was 2.0, but the concentration of CO was higher with ${\mathrm{CO}}_2$ as the GA, reaching 72% at a ${\mathrm{CO}}_2/\mathrm{C}$ ratio of 2.0. The net ${\mathrm{CO}}_2$ emissions and the ${\mathrm{H}}_2/\mathrm{CO}$ ratio were lower with ${\mathrm{CO}}_2$ and higher with ${\mathrm{H}}_2\mathrm{O}$ as the GA. The concentration of CO in the syngas was the highest (72%) when the temperature was 1,600 K and the ${\mathrm{CO}}_2/\mathrm{C}$ ratio was 0.4, making it suitable as a raw material for the production of formic acid. The results of this study offer guidelines for the optimization of the operating parameters for entrained flow gasification and the application of ${\mathrm{H}}_2\mathrm{O}$ and ${\mathrm{CO}}_2$ gasification technology.

Pore structural parameters of coalbed methane (CBM) reservoirs can be vital in establishing their gas adsorption behaviors and gas transport capabilities. The fractal dimension of pore structure in coals by low-field nuclear magnetic resonance (LFNMR) measurements are not well understood. The LFNMR parameters, including the amplitude weighted mean on a logarithmic dimension (T2g), the highest periodicity on the transverse relaxation time (T2) spectrum (T2p), bulk volume irreducible (BVI), free fluid index (FFI), and the radio of BVI and FFI, for seven coal rank samples were determined. Accessible pore and inaccessible pore are distinguished and porosity-related characteristics were investigated. We propose a novel model to obtain the fractal dimension of three type of pores by the LFNMR. The accuracy of this model was validated by the statistically significant correlations. We suggested fractal dimensions 2.65, 2.93, and 2.86 corresponding to accessible, inaccessible, and total pores, respectively. Results of fractal dimension analysis were purposed for the investigation of the relationship between petrophysics properties and pore structure heterogeneity. The fractal dimension of accessible pores presented distinctly correlated with the T2p and connectivity. Moreover, the strong positive relationship between fractal and vitrinite reflectance documented that that accessible pore characterization had contributed to lower fractal dimensions and less complex pore structure in the lower metamorphic coals. Consequently, this method is a new theory that can be used to better understand the heterogeneity of accessible, inaccessible, and total pores. These findings contribute to the further understanding of pore structural heterogeneity and provide practical guidelines to achieve gas storage properties and gas transport behaviors, which are critical but much-needed for CBM reservoirs.

A three-dimensional model of anode-supported planar solid oxide fuel cells (SOFCs) was developed. The output performance, temperature distribution, and concentration distribution of a SOFC stack were studied when the pitch width and rib width on the anode side of the SOFC model were different from those on the cathode side. The results showed that under the same voltage, SOFC performance can be improved by using a smaller connector rib width on the cathode side than on the anode side, and the output power can be increased by 24%. It was also found that under the same conditions, the larger pitch width of the connector on the anode side and the smaller pitch width on the cathode side could improve the output performance of SOFCs. The research provides a reference for the design optimization of SOFC stacks.

The objective of this study is to numerically investigate the effects of the combustion chamber (CC) shapes on the combustion and emission characteristics of the n-octanol fueled compression ignition (CI) engine. At first, to enable the simulation of the combustion process for n-octanol, the skeletal mechanism that consists of 87 species and 422 reactions was obtained by applying directed relation graphs (DRG), directed relation graphs with error propagation (DRGEP), sensitivity analysis, isomer lumping, and peak concentration analysis to the detailed n-octanol reaction mechanism. Then, it was coupled with KIVA4-CHEMKIN to study the effects of the CC shapes for the n-octanol fueled CI engine. In this study, based on the original CC, five different shapes of CCs were designed by changing the parameters of throat radius, depth, and shrinkage angle while keeping the compression ratio constant. Simulations with different CCs were carried out at 1,500 and 2,400 rpm, respectively. The results show that the CCs with different throat radii have important effects on combustion and emission characteristics. As increasing the throat radius, the peak heat release rate (HRR) and the pressure rise rate (PRR) increase at 1,500 rpm; in comparison, both peak HRR and PRR show an opposite trend at 2,400 rpm. Moreover, as widening the throat, the indicated mean effective pressure (IMEP) of 2,400 rpm can be improved significantly due to the extension of combustion duration. For emissions, both narrow and wide throats could generate less NOx under 1,500 and 2,400 rpm. Also, ultra-low soot emissions are realized by fueling n-octanol. As for CO emissions, with the increase of throat radius, it decreases first and then increases at 1,500 rpm; in contrast, a decreasing trend can be observed due to the prolonged combustion duration. Considering the best performance of the engine, NO.5 CC with a radius of 25.75 mm can be selected.

When technological innovations are implemented in the wind energy sector, one should observe reductions in the production cost of electricity. However, the accuracy of inferring the rate of innovation from production cost reductions is open to challenge when those costs change due to factors not attributable to technological innovation. To control for such factors, this study applies engineering models and derives production cost reduction trends through changes in the technical parameters of wind turbines. The obtained innovation measure is unlikely to be affected by the noninnovative determinants of the production cost as long as the underlying engineering models are accurate. The usefulness of the generated innovation measure is illustrated in the context of learning curve literature.

Virtual reality (VR) modeling has been increasingly applied in the field of construction; however, research and progress in the design and evaluation of energy-saving building envelopes of residential structures in northern cold regions remain limited. The objective of this study is to identify less laborious methods to analyze the design of energy-efficient buildings using a back-propagation neural network (BPNN) combined with VR modeling to evaluate the building envelope structure. We first use the BPNN to construct an algorithm to calculate the heat transfer coefficient of the building envelope. Houses in Harbin are used as research objects, and a VR platform is used to construct architectural models of different envelopes. Results show that the error of the BPNN algorithm applied to the heat transfer coefficient identification of the building envelope is less than 5%, and that the VR software can realize the three-dimensional modeling of different energy-saving envelope structures, as well as facilitate the evaluation of energy-saving performance. The results can provide a theoretical basis for designers and decision-makers in the application of the BPNN and VR technology to the design and evaluation of building energy conservation.

Cofiring biomass syngas (BS) with pulverized coal under the oxy-fuel condition is a promising technology, which could encourage the utilization of biomass energy and reduce the emission of greenhouse gases. To investigate cofiring characteristics of biomass syngas and coal, a numerical study was conducted. The influences of oxy-fuel condition, syngas quality, and injection position on temperature distributions and flue gas components in boiler furnace were analyzed. To predict cofiring characteristics accurately under oxy-fuel conditions, a new refined weighted-sum-of-gray-gases model, HCN oxidation model, and NO-char reaction model were used. The simulation results show that syngas reburning and oxy-fuel conditions could reduce NO emission. The NO emission in ${\mathrm{O}}_2/{\mathrm{CO}}_2$ conditions is higher than that in air. Biomass syngas with higher calorific values contributes to higher furnace temperatures. Besides, biomass syngas with higher hydrocarbon components is beneficial to lower NO emission. Compared to pure coal combustion, NO concentration at the furnace outlet reduces by 40.2%, 69.0%, and 35.2% in the cases of cofiring with Type A, B, and C biomass syngas at a cofiring ratio of 10%, respectively. The injection position of biomass syngas also has crucial impacts on cofiring characteristics and NO emissions. NO emission has the lowest value when the biomass syngas is injected at the bottom level of the reburn zone. This study could provide a reference for optimization of boiler design and operation when cofiring biomass syngas with pulverized coal under the oxy-fuel condition.

A theoretical study was conducted to assess the impact of intake-air nitrogen enrichment on the performance characteristics and soot and nitrogen oxide (NO) emissions of a high-speed direct injection (HSDI) single-cylinder diesel engine. Closed-cycle engine simulation code based on a multizone combustion model was used to perform simulations considering three fuels—diesel fuel, Jet Propellant-8 (JP-8), and Rapeseed Methyl Ester (RME)—and four intake-air nitrogen contents—79%, 81%, 83%, and 85% v/v, at an engine speed of 1,200 rpm under various loads. Two modes of diesel engine combustion are considered: constant fueling rate and, hence, rich in-cylinder average fuel/oxygen equivalence ratio (RAFOER) and reduced fueling rate and, hence, constant average fuel/oxygen equivalence ratio (CAFOER). Theoretical results for the cylinder pressure, bulk gas temperature, and in-cylinder soot and NO concentrations are derived at a speed of 1,200 rpm, and at full engine load considering the three examined fuels and the four examined intake-air nitrogen contents. Predictions for the percentage change of indicated power, exhaust gas temperature, exhaust soot, and exhaust NO are also generated for all examined fuels and at all examined nitrogen contents. The most important findings of the present study are as follows. First, intake-air nitrogen enrichment results in the achievement of NO emissions–free diesel operation, while on the other hand, for increased air nitrogen contents, the extremely limited soot oxidation rate keeps the exhaust soot at high levels. Second, under CAFOER conditions, the combustion of a well-known biodiesel such as RME can lead to reductions in both soot and NO emissions, thereby improving the well-known diesel engine soot/NO trade-off limitation.

Power infrastructure is essential for the operation of almost all other critical infrastructure systems, including water, transportation, and telecommunications. Recently, there has been an increase in forced power outage frequency and extent due to infrastructure aging, extreme weather events, and deliberate attacks. To combat forced power outage risks, researchers have been focusing on improving the resilience of different power infrastructure systems. A key aspect of infrastructure resilience is the rapidity, defined as the time required to return to normal operation levels following functionality disruptions. This study developed a machine learning–based framework to predict the rapidity of power infrastructure following forced outages. The framework includes classification models such as bagging, random forests, and artificial neural networks to accommodate the categorical nature of typical power infrastructure component outage features. The framework also includes a genetic algorithm for optimized selection of such features in order to facilitate the model’s best prediction performance. The utility of the developed framework was demonstrated using actual transmission line forced outages data. Within the demonstration application, rapidity was split into two classes indicating short and extended outages, and the random forest classification model had the best rapidity prediction performance. In addition, the influence of key features on outage classification was explored using partial dependence analysis. Finally, insights for resilience-guided asset management were presented. The developed framework enables infrastructure stakeholders to predict forced outage rapidity classes soon after the occurrence of the former—subsequently enabling rapid identification of appropriate resources needed to promptly restore infrastructure functionality and thus ensuring infrastructure resilience.

The battery thermal management system (BTMS) plays an important role in maintaining the optimal working temperature range and temperature uniformity of batteries. In this study, a novel liquid cooling BTMS for a cylindrical 21700-type battery is proposed. It uses straight microtubes and heat conduction blocks with a variable contact surface. An orthogonal test and multifactor analysis are used to determine the primary and secondary effects of the key structural and operating parameters on the cooling performance of the battery module. The results indicate that the working condition parameter of the BTMS, i.e., the liquid coolant flow rate ($q$), has a greater impact on the temperature performance of the batteries than the structural parameters [including the variable contact angle ($\mathrm{\Delta }\alpha$) and height ($h$) of the heat conduction blocks and the diameter of the straight microtubes ($d$)]. $\mathrm{\Delta }\alpha$ is the most important structural parameter and has a significant influence on the temperature uniformity of the batteries. In the optimal combination model, $\mathrm{\Delta }\alpha$ is 12°, $h$ is 52 mm, $d$ is 2.0 mm, and $q$ is $0.2\mathrm{L}/\min$. Under these conditions, the maximum temperature and the maximum temperature difference are 34.6°C and 2.9°C, respectively, at the end of the 2C discharge cycle.

In the era of automation and artificial intelligence, some problems in the petroleum industry can be efficiently solved by machine-learning models based on big data. Previous researchers have addressed truly precise methods to evaluate reserves from accessible well-test data, although enormous time, effort, and cost were required. However, due to the lack of sufficient conventional material balance (CMB) test data for a large number of wells in the Su Dong (SD) gas field, only 12 wells (0.99%) were generally suitable for previous methods. We substituted the fully built-up reservoir pressures used in CMB with those converted from shut-in casing-pressure data. Suffering from data noise caused by pressure fluctuation, only 22.91% of the plots were linear. The nonlinear plots were enhanced into linear plots by denoising and smoothening according to engineering knowledge. Machine-learning models were developed to perform the enhancement automatically, which constructed an adaptive and feasible workflow. The model improved the percentage of applicable wells to 68.16% (824 out of 1,209) and achieved considerable precision for reserve evaluation. The proposed method is much more suitable than CMB in gas fields featuring unstable production status and provides feasible reserve evaluation. This paper presents an exemplary demonstration of the combination of petroleum engineering and machine learning.

There is a tendency toward larger transparent envelopes in architectural design, which could causes excessive energy consumption and visual discomfort. Using advanced shading systems with control strategies could potentially improve the indoor environmental quality and reduce the building energy consumption. In this research, daylight and thermal performances of a double-skin facade (DSF) system with multisectional shading devices using combinations of roller shades and blinds along with different control strategies were investigated under three representative climatic conditions of China. The performance of DSF systems with and without shading devices were comparatively analyzed by evaluating the annual heating, cooling, and artificial lighting energy consumption, daylight availability, and visual discomfort index using EnergyPlus and Radiance. The results indicated that the DSF with a multisectional shading system and proper control algorithms can save up to 6.8% and 4.8% for annual building energy consumption in Xiamen and Shanghai, respectively, but slightly increased by 2.4% in Beijing, when compared with the baseline without any shading device. For daylight performance, the multisectional shading system with proper control strategy shows a significant contribution to eliminating annual sunlight exposure (ASE) and discomfort glare while maintaining the daylight availability at a similar level compared to the system without shading. Meanwhile, the combination could improve spatial daylight autonomy (sDA) by 11.9%, 16.7%, and 19.1% in Xiamen, Shanghai, and Beijing, respectively, when compared with the individual shading system.