Potential For Improving Efficiency Research Articles

An examination of labor time-use on spring-calving dairy farms in Ireland

The seasonal workload associated with pasture-based dairy farms, combined with increasing herd sizes, has led to a renewed focus on labor time-use and efficiency on dairy farms. The objective of this study was to examine labor time-use on pasture-based dairy farms in the spring and summer seasons. A total of 82 spring-calving Irish dairy farms completed the study from February 1 to June 30, 2019 (150 d). Each farmer recorded their labor input on one alternating day each week using a smartphone app. Any labor input by farm workers not using the app was recorded through a weekly online survey. Farms with data for each month (n = 76) were classified into 1 of 4 herd size categories (HSC) for analysis: farms with 50 to 90 cows (HSC 1); 91 to 139 cows (HSC 2); 140 to 239 cows (HSC 3); and ≥240 cows (HSC 4). Total hours of labor input was similar on HSC 1 (1,821 h) and HSC 2 (2,042 h) farms, but predictably as HSC increased further, total hours of labor input increased (HSC 3: 2,462 h, HSC 4: 3,040 h). On a monthly basis, labor input peaked in February (15.4 h/d) and March (15.7 h/d). The farmer worked on average 60.0 h/wk over the duration of the study period. Hired labor and contractors completed a greater amount of work as HSC increased. Labor efficiency, as measured by hours/cow, improved as HSC increased (HSC 1: 26.3 h/cow, HSC 2: 17.7 h/cow, HSC 3: 14.3 h/cow, HSC 4: 10.9 h/cow), though there were large variations in labor efficiency within HSC. Milking was the most time-consuming task, representing 31% of farm labor input making it an important focus for potential improvements in efficiency. The next 5 most time-consuming tasks were calf care (14%), grassland management (13%), cow care (10%), repairs and maintenance (10%), and administration/business (8%). This study contributes to the understanding of labor use during the busiest (most labor demanding) time of the year on pasture-based dairy farms and points to areas where labor efficiency improvements can be made on farms. The considerable variation in farm labor efficiency observed within HSCs emphasizes the necessity for a greater focus on knowledge transfer of methods to achieve improved labor efficiency and a better work-life balance on many dairy farms. As the 2 busiest months on most dairy farms, February and March require the most focus for identification of potential labor savings.

Predictive modeling of a subcritical pulverized-coal power plant for optimization: Parameter estimation, validation, and application

As renewable power generation deployment increases, fossil fuel plants are increasingly required to operate more flexibly. Many coal-fired power plants were originally designed to operate at base load and do not operate optimally at partial load. Predictive first-principles plant-wide models can be employed to identify opportunities for flexibility improvements and diagnose low-load operating issues. This paper describes the application of the Institute for the Design of Advanced Energy Systems Integrated Platform (IDAES) to model and optimize flexible power plant operations. The key benefits of using IDAES are that it provides an open-source, fully equation-oriented modeling framework for efficient modular model construction, reuse, and customization, together with a mathematical optimization framework leveraging powerful, state-of-the-art solvers. The process systems engineering workflow from predictive process simulation to parameter estimation, model validation, and plant optimization is applicable to a variety of existing and next-generation energy systems as well as other chemical and environmental processes. To demonstrate this capability, a physics-based, steady-state model was developed to improve full- and part-load performance of the Escalante Generating Station, a 245 MWe (net) subcritical pulverized coal-fired power plant owned and operated by Tri-State Generation and Transmission Association. Specifically, sixty-nine model parameters were simultaneously estimated from several months of operating data enabling prediction of flow rates, temperatures, pressures, and steam quality throughout the plant. The validated model was leveraged by Escalante to reduce the minimum operating load from 90 MW to 50 MW by diagnosing a low-load water-hammer issue, enabling coal usage and emissions reductions during periods of low power demand. Additionally, opportunities for heat rate reduction (i.e., efficiency improvement) through a steeper sliding-pressure approach to load-following and optimization of other boiler operating variables were also identified and quantified. For example, a potential efficiency improvement of 0.7 percentage points was observed at half-load operation.

Exergy Analysis of the Prevailing Residential Heating System and Derivation of Future CO2-Reduction Potential

The residential heating sector accounts for a large share of the worldwide annual primary energy consumption. In order to reduce CO2-emissions, it is therefore particularly important to analyse this sector for potential efficiency improvements. In Europe, natural gas boilers are the most widely used heating technology since they are cost-effective and can be installed in any type of building. The energy efficiency of these boilers is already high. However, in their internal process, heat is generated at a high temperature level which is only used for space heating and therefore a high amount of exergy remains unused. This research aims to develop the potential of using the exergy to further improve the efficiency of the systems. A novel combination of methods is applied to analyse the thermodynamic behaviour of gas-fired boilers in detail and over the cycle of a year. The analysis is performed in two steps: In the first step a system is examined in stationary operating points. This is carried out through an experimental setup and a three-dimensional numerical simulation. In the second step, the obtained data is applied to a transient annual building simulation. The results show the temporal distribution and total amount of the annual exergy loss for a common residential building. The exergy loss accumulates to 16,271 kWh per year, which shows the high potential to partially convert the exergy to electrical energy and significantly reduce the external electricity demand and CO2-emissions of the building. Based on this, new technologies such as Thermoelectric Generators can be developed, which can enable this potential.

Development of self-powered multifunctional piezomagnetic nanoparticles for non-invasive post-surgical osteosarcoma theranogeneration

Non-invasive treatment approaches are gaining increased attention in recent times due to the potential improvement in treatment efficiency. Here we report a brand-new ultrasound (US), magnetic field (MF), and light responsive nanoparticle system based on piezoelectric barium titanate and superparamagnetic iron oxide nanoparticles termed as PiezoMagnetic nanoparticles (PMNPs) for the multimodal anticancer treatment, complimentary imaging, and non-invasive cell stimulation. Systematic electrical studies reveal that PMNPs exhibit superior ferroelectric and conducting properties and produce electrical signals analogous to native tissues when mechanically stimulated and using Food and Drug Administration (FDA) approved Low intensity Pulsed Ultrasound (LIPUS) non-invasively for cell stimulation. The green energy generation from PMNPs are further proved by the real-time demonstration of commercial LED lit-up. Thus, as a proof of concept, we have demonstrated a stepwise treatment efficacy of PMNPs for the post-surgical osteosarcoma therapy, diagnosis, and regeneration (termed as Theranogeneration). The PMNPs exhibited excellent anticancer properties to eradicate the residual osteosarcoma cells by magnetic hyperthermia, photothermal and photodynamic therapy along with complimentary imaging capabilities as X-ray computed tomography contrast agent and label free fluorescent probe. The conjugation of risedronate R (RIS)- a new generation bisphosphonates to PMNPs imparted bone seeking property to PMNPs. Therefore, as a result of noninvasive cell stimulation properties of PMNPs under LIPUS and LIPUS mediated RIS release from PMNPs resulted in enhanced bone regeneration and bone turnover which helps to repair the large bone defects occurs in osteosarcoma resection. Thus, the study corroborates the development of a potential bioactive piezoelectric nanoplatform that can be used to control the cell behaviour and fate in order to develop new types of therapies for multiple diseases including various types of cancers in remotely controlled approaches.

Holistic energy flow analysis of a solar driven thermo-chemical reactor set-up for sustainable hydrogen production

In this contribution, a holistic energy flow analysis of a solar driven pilot plant for green hydrogen production using two-step thermochemical cerium-based redox cycles is carried out. The plant consists of a heliostat field, a large-scale inert gas reactor, an efficient fluid heat recovery system and an electrical vaporizer for steam generation. The system behaviour is physically described, and energy flows are quantified using a complex simulation model considering material and geometric properties of the complete system design. The system energy flow and corresponding impact on plant efficiency is thoroughly analysed with emphasis on plant design, operational strategy, and influence of the crucial control parameters. Influences of a heat recovery system and the size of various types of heat losses are investigated, potential efficiency improvements are revealed and useful possibilities for plant design and material modifications are discussed. The transient system behaviour is investigated by varying temperatures and mass flow rates in a broad practicable range to gain more insight in efficient reactor design and plant control. Two temperature swing strategies are investigated in more detail, which are by far more efficient than any near-isothermal or isothermal strategy for this application.

The potential in efficiency improvement of in-cylinder thermochemical fuel reforming in natural gas engines

With decarbonization becoming the primary focus of the automotive industry, high-efficiency and low-emission engine technologies have garnered increasing attention. Among the advanced technologies, fuel reforming is a promising way to reduce emissions and increase efficiency with hydrogen generated onboard. This paper focuses on the potential of improving efficiency using in-cylinder thermochemical fuel reforming combustion in natural gas engines. The experimental results revealed that combustion in the reforming cylinder inevitably deteriorates, and hydrogen generated by fuel reforming could not counteract the slower combustion due to fuel enrichment. However, the presence of hydrogen in the nonreforming cylinder significantly increased the flame propagation speed, hence improving combustion efficiency. After a series of variable-parameter tests, it was found that the appropriate reforming equivalence ratio was 1.15 at low load and ∼1.2–1.3 at medium to high loads, with efficiency increased up to 4–5% and 8–12%, respectively. Dual-fuel reforming combustion experiments were conducted using a rapid compression machine and a single-cylinder engine to further explore the fuel reforming combustion. The results suggested that, at medium load, the combustion instability in the reforming cylinder can be solved by adopting two-stage combustion at an equivalence ratio of 1.3, and the indicated thermal efficiency could reach 43%. Based on the research results, a remarkable potential improvement in efficiency can be achieved if reforming combustion is successfully implemented. Dual-fuel reforming has the most potential to achieve an even higher engine efficiency, and its practical issues such as transient engine control and soot formation deserve further investigation.

Energy Efficiency Optimization for Machining of Wood Plastic Composite

Enhancing energy efficiency is the key to realizing green manufacturing. One major area of interest in this regard is the improvement of energy efficiency of machine tools during the production of building materials. This project focuses on energy efficiency during the spiral milling of wood plastic composites. To this end, a response surface method was adopted to develop a model and establish the relationship between energy efficiency and milling conditions. Analysis of variance based on individual factors as well as two-factor interactions was performed to gauge their effects on energy efficiency. It was found that milling depth was positively correlated to power efficiency, while spiral angle and feed per tooth displayed non-monotonic behavior. An attempt was made to predict milling conditions that will yield the greatest material removal rate and power efficiency. For wood plastic composites subjected to up-milling, it was determined that a feed per tooth of 0.1 mm, milling depth of 1.5 mm, and spiral angle of 70° were ideal. Considering the potential improvements in energy efficiency and surface quality that these process parameters will bring, it is strongly recommended for use in the industrial machining of wood plastic composites.

An overview of machine learning applications for smart buildings

The efficiency, flexibility, and resilience of building-integrated energy systems are challenged by unpredicted changes in operational environments due to climate change and its consequences. On the other hand, the rapid evolution of artificial intelligence (AI) and machine learning (ML) has equipped buildings with an ability to learn. A lot of research has been dedicated to specific machine learning applications for specific phases of a building's life-cycle. The reviews commonly take a specific, technological perspective without a vision for the integration of smart technologies at the level of the whole system. Especially, there is a lack of discussion on the roles of autonomous AI agents and training environments for boosting the learning process in complex and abruptly changing operational environments. This review article discusses the learning ability of buildings with a system-level perspective and presents an overview of autonomous machine learning applications that make independent decisions for building energy management. We conclude that the buildings’ adaptability to unpredicted changes can be enhanced at the system level through AI-initiated learning processes and by using digital twins as training environments. The greatest potential for energy efficiency improvement is achieved by integrating adaptability solutions at the timescales of HVAC control and electricity market participation.

Solution-Processed, Inverted AgBiS2 Nanocrystal Solar Cells.

AgBiS2 nanocrystals are a promising nontoxic alternative to PbS, CsPbI3, and CdS quantum dots for solution-fabricated nanocrystal photovoltaics. In this work, we fabricated the first inverted (p-i-n) structure AgBiS2 nanocrystal solar cells. We selected spray-coated NiO as the hole-transporting material and used PCBM/BCP as the electron-transporting material. Combining transient photocurrent and photovoltage measurements with femtosecond transient absorption spectroscopy, we investigated the charge collection process on metal oxide/AgBiS2 interfaces and demonstrated that the NiO/AgBiS2 NC junction in the p-i-n configuration is more efficient for charge carrier collection. The fabricated p-i-n solar cells exhibited a 4.3% power conversion efficiency (PCE), which was higher than that of conventional n-i-p solar cells fabricated using the same sample. Additionally, inverted devices showed an ultrahigh short-circuit current (JSC) over 20.7 mA cm-2 and 0.38 V open-circuit voltage (VOC), suggesting their potential for further improvements in efficiency and, eventually, for large-scale production.

Application of solid lubricant for enhanced frictional efficiency of deep drawing process

Manufacturing processes are usually energy intensive, contributing to the global carbon dioxide emissions. Deep Drawing is one of the most common types of sheet metal forming processes with great potential for energy efficiency improvement. In this paper, the optimised combination of molybdenum disulphide (MoS2) and graphite is proposed as a solid lubricant to reduce the punching force and energy consumption of deep drawing process. Different mixtures of MoS2 and graphite are prepared and their tribological performance are measured using experimental tests on tribometer. In order to investigate the friction reduction rate in deep drawing process, finite element simulation of the drawing process is performed. Results show that friction reduction using proposed combination of lubricants has significant effect on punching force and would provide greater process efficiency.

Optimization of oxy-fuel circulating fluidized bed combustion system with high oxygen concentration

High oxygen concentration oxy-fuel circulating fluidized bed (CFB) combustion technology is a high efficiency and low cost oxy-fuel combustion technology, which also has high potential of optimization and efficiency improvement. In this research, energy efficiency is analysed to acquire the effects of flue gas recirculation mode, preheating temperature and oxygen purity on the net efficiency and energy consumption of the 50% oxy-fuel combustion system, so as to obtain the further optimization strategy. The following results are obtained by simulation. The optimal extraction position of recycled flue gas (RFG) is gas preheater outlet, and the net power generation efficiency increases by 0.23% to 25.08%. As the preheating temperature increases from 230 to 400 °C, the net efficiency increases by 1.19% to 26.04%. The net efficiency increases first then decreases as the oxygen purity at ASU outlet increase from 89% to 97.4%, and the optimal value is the 94%. Finally, the optimal system is obtained when RFG is extracted from gas preheater outlet, the preheating temperature is 400 °C and the oxygen purity at ASU outlet is 94%. The net efficiency of the optimization system reaches 27.4%, increasing by 2.55%.

Efficiency Improvement in Sugar Mills through Bagasse Gasification

Biomass energy is useful in mitigating greenhouse gases as biomass growth captures CO2 from the atmosphere. This captured CO2 is eventually released upon the use of these biomass feedstocks, leading to a short duration carbon cycle. One prominent biomass feedstock is the sugarcane bagasse that is a by-product of sugar mills. Conventionally, the bagasse produced after crushing sugarcane is utilized in mills by directly burning it in the boilers. However, an efficient way of utilizing this biomass fuel would be through gasification. The biomass gasification process produces syngas which can be used as fuel for meeting the heat and power demands of the sugar mill, as well as surplus electricity, which can be supplied to the grid. The present study aims to highlight the efficiency improvement potential of bagasse gasification. The entire sugar mill as well as the bagasse gasification process, have been modeled in the ASPEN Plus® software. Heat integration has been performed to optimize the process flowsheets. The electrical energy generated by combusting bagasse is estimated to be 0.57 kWh/kg of bagasse combusted. In contrast, the gasification process can increase this energy generation up to 1.16 kWh/kg of bagasse at 10 % moisture content (by wt.). This study aims to provide awareness about the cleaner and efficient utilization of the energy that lies dormant in these industrial residues.

Exergy Analysis of Coal-Based Series Polygeneration Systems for Methanol and Electricity Co-Production.

This paper quantifies the exergy losses of coal-based series polygeneration systems and evaluates the potential efficiency improvements that can be realized by applying advanced technologies for gasification, methanol synthesis, and combined cycle power generation. Exergy analysis identified exergy losses and their associated causes from chemical and physical processes. A new indicator was defined to evaluate the potential gain from minimizing exergy losses caused by physical processes—the degree of perfection of the system’s thermodynamic performance. The influences of a variety of advanced technical solutions on exergy improvement were analyzed and compared. It was found that the overall exergy loss of a series polygeneration system can be reduced significantly, from 57.4% to 48.9%, by applying all the advanced technologies selected. For gasification, four advanced technologies were evaluated, and the largest reduction in exergy loss (about 2.5 percentage points) was contributed by hot gas cleaning, followed by ion transport membrane technology (1.5 percentage points), slurry pre-heating (0.91 percentage points), and syngas heat recovery (0.6 percentage points). For methanol synthesis, partial shift technology reduced the overall exergy loss by about 1.4 percentage points. For power generation, using a G-class gas turbine decreased the overall exergy loss by about 1.6 percentage points.

Guest Editorial: Special Issue on Magnetically Levitated Motor Systems

Magnetically levitated motor systems based around bearingless motors and generators and magnetic bearings offer a potential step change improvement in motor system lifetime and efficiency, solve critical challenges related to oil-lubricated contact bearings, and feature prognostic capabilities for condition-based maintenance. Acceptance of this technology has been historically limited by challenges related to cost, design and control complexity, manufacturing issues, and low force density. However, recent advances in supporting technologies enable new approaches to address these challenges.

How important are perceived barriers and drivers versus other contextual factors for the adoption of energy efficiency measures: An empirical investigation in manufacturing SMEs

This study examines determinants of adoption of energy-efficiency measures (EEMs) in small and medium-sized enterprises (SMEs) in the manufacturing sector, where the energy-efficiency gap is more likely to persist according to findings in previous literature. By simultaneously examining the relevance of multiple groups of perceived barriers and drivers and several other objectively measurable contextual factors, this research aims to fill the noted gap in econometric evidence. Discrete choice models are applied to a representative sample of 220 Slovenian manufacturing SMEs, comprising 10% of all manufacturing SMEs in the country to validate the relevance of barriers and drivers for both past adoptions of EEMs and firms’ plans for adoption in the future. In line with previous empirical findings, results show that economic incentives, namely cost reductions for past investments and the potential for energy savings for future investments, are among the key drivers, while limited financial resources in more indebted firms blocked decisions to implement EEMs. Interestingly, energy-efficiency-related determinants play a more significant role than other self-perceived barriers and drivers. The importance firms place on energy efficiency, running energy-efficiency awareness programs for employees in the firms and obtaining information through external advice or energy audits triggered past company investments. Energy-intensive and innovative firms that are more aware of the importance of energy efficiency and those that have carried out investments in the past are more prone to adopt EEMs in the future, also confirming the path-dependency of energy efficiency activities. Lessons for policy-makers and managerial implications lie in spreading awareness about energy efficiency among managers and employees, building competence, and providing information on the potentials for energy efficiency improvements and on the availability of public and private funds.

Thermodynamic Evaluation of the Forced Convective Hybrid-Solar Dryer during Drying Process of Rosemary (Rosmarinus officinalis L.) Leaves

This study aimed to examine the energy and exergy indices of the rosemary drying process in a hybrid-solar dryer (HSD) and the effects of air-drying parameters on these thermodynamic indices. Drying experiments were carried out at four levels of air temperature (40, 50, 60, and 70 ∘C) and three levels of air velocity (1, 1.5, and 2 m/s). Energy and exergy were calculated by application of the first and second laws of thermodynamics. Based on the principal laws, energy efficiency, exergy losses, and exergetic improvement potential rate, were evaluated. The results showed that the energy utilization ratio (EUR) ranged from 0.246 to 0.502, and energy utilization (EU) ranged from 0.017 to 0.060 (kJ/s). Exergy loss and efficiency varied from 0.009 to 0.028 (kJ/s) and from 35.08% to 78.5%, respectively, and increased with increased temperature and air velocity. It was found that the exergy loss rate was affected by temperature and air velocity because the overall heat transfer coefficient was different under these conditions. By comparison, with increasing temperature and air velocity, the exergy efficiency increased. Because most energy is used to evaporate moisture, this behavior may be explained by improved energy utilization. The drying chamber sustainability index ranged from 0.0129 to 0.0293. This study provides insights into the optimization process of drying operations and operational parameters in solar hybrid dryers that reduce energy losses and consumption.

Automated and Controlled Data Collection Using Industrial IoT System for Smart Maintenance

Maintenance 4.0 is a concept that involves the use of IIoT (Industrial Internet of Things) technology to connect maintenance objects, which enables remote data collection, information exchange, analysis and potential improvement in productivity and efficiency, as well as planning maintenance activities. The purpose of this paper is to present the development of the Industrial Internet of Things data collection system, which relies on Tinkerforge IoT modules, that enables automated data collection alongside control of sensor and data collection parameters. To evaluate the ability of the system, an experiment was conducted where two equipment states were simulated using a rotational equipment failure simulator. The experiment determined that the presented IIoT system had successfully gathered information and that there is a clear distinction in acceleration patterns when simulating two different equipment states.

Potential efficiency improvement of CuSb(S1-x,Sex)2 thin film solar cells by the Zn(O,S) buffer layer optimization

An initial structure was designed based on CuSbS2 experimental data. Afterward, an investigation on the performance of contact/CuSb(S1-x,Sex)2 alloy/buffer layer/i-ZnO/ZnO:Al/contact has been carried out. The conventional CdS layer was replaced by Zn(O1-y,Sy) layer, and the influence of Se/(Se + S) ratio in the CuSb(S1-x,Sex)2 absorber layer and oxygen content in the buffer layer on the performance of the solar cell were investigated. The influence of carrier concentration of both CuSb(S1-x,Sex)2 absorber and buffer layers are studied to enhance the output performance of CuSb(S1-x,Sex)2 thin-film solar cells. After optimizing the Se content in the absorber layer and oxygen content in the buffer layer, the best device is delivered 5.85% efficiency. However, after optimization of donor concentration in buffer layer the conversion efficiency is further increased to 7.19% for CuSbS2 absorber and donor concentration of 1019 cm−3.

Research on the potential of water resources utilization efficiency in the North Canal Basin to improve environmental flow guarantee degree

North Canal is one of the important tributaries of Haihe River, however, it can’t meet the requirement of environmental flow. Improving water resources utilization efficiency is one significance way to enhance the environmental flow guarantee degree, Hence, it’s significant to study the potential of improving water use efficiency of the North Canal Basin. Based on the Analytic Hierarchy Process, this paper comprehensively evaluates the water use efficiency of the North Canal Basin from 2016 to 2019, and analyses the water use efficiency improvement potential. The results show that the water use efficiency of the North Canal Basin is high compared to the national average level, and the water use efficiency of the basin is increasing from 2016 to 2019, and the comprehensive score of water use efficiency in 2019 is the highest at 0.927, and lowest in 2016 at 0.896. The water use efficiency of the North Canal Basin still has the potential to be improved, and with the promotion of ecological civilization construction in China, the environment water consumption in the North Canal Basin is increasing year by year, and the environmental flow guarantee degree of the North Canal Basin will be gradually improved.

Improving Sampling Efficiency for Determining Pediatric HIV Prevalence in National Surveys: Evidence From 8 Sub-Saharan African Countries.

Measurement of mother-to-child HIV transmission through population-based surveys requires large sample sizes because of low HIV prevalence among children. We estimate potential improvements in sampling efficiency resulting from a targeted sample design. Eight countries in sub-Saharan Africa with completed Population-based HIV Impact Assessment (PHIA) surveys as of 2017. The PHIA surveys used a geographically stratified 2-stage sample design with households sampled from randomly selected census enumeration areas. Children (0-14 years of age) were eligible for HIV testing within a random subsample of households (usually 50%). Estimates of child HIV prevalence in each country were calculated using jackknife replicate weights. We compared sample sizes and precision achieved using this design with a 2-phase disproportionate sample design applied to strata defined by maternal HIV status and mortality. HIV prevalence among children ranged from 0.4% (95% confidence interval: 0.2 to 0.6) in Tanzania to 2.8% (95% confidence interval: 2.2 to 3.4) in Eswatini with achieved relative standard errors between 11% and 21%. The expected precision improved in the targeted design in all countries included in the analysis, with proportionate reductions in mean squared error ranging from 27% in Eswatini to 61% in Tanzania, assuming an equal sample size. Population-based surveys of adult HIV prevalence that also measure child HIV prevalence should consider targeted sampling of children to reduce required sample size, increase precision, and increase the number of positive children tested. The findings from the PHIA surveys can be used as baseline data for informing future sample designs.