Potential Evaluation Method Research Articles

Data-driven and physical model-based evaluation method for the achievable demand response potential of residential consumers' air conditioning loads

Demand response (DR) of thermostatically controlled load (TCL) represented by residential consumers' air conditioning (AC) is playing an increasingly important role in enhancing the operational flexibility and sustainability of the power grid. The evaluation of the DR potential of AC is of great significance in the power grid dispatching and the selection of DR target consumer groups. However, due to the large number of AC units, the large difference in parameters, and the randomness and uncertainty of consumers' electricity consumption behavior and DR willingness, it is difficult to accurately evaluate the achievable potential of the AC. In order for power system operator (PSO) or load aggregator (LA) to better mine and manage the AC resources, this paper proposes a systematic method suitable for evaluating the achievable DR potential of a single residential AC and aggregate residential ACs the next day. Firstly, based on the parameter identification of equivalent thermal parameter (ETP) model, a method for estimating the theoretical time-varying potential of the AC is proposed. Then considering the influence of the randomness of residents' electricity consumption behavior on the DR potential, an AC state prediction model is constructed based on the deep learning network. On this basis, social psychology and fuzzy systems are used to quantify the impact of residential consumers' DR willingness on the DR potential, and an achievable potential evaluation model is constructed. Finally, based on the actual load data of the Muller project in Austin, the effectiveness and accuracy of the proposed DR potential evaluation method are further demonstrated.

From salt to hypertension, what is missed?

Excess salt intake is viewed as a major contributor to hypertension and cardiovascular disease, and dietary salt restriction is broadly recommended by public health guidelines. However, individuals can have widely varying physiological responses to salt intake, and a tailored approach to evaluation and intervention may be needed. The traditional sodium related concepts are challenging to assess clinically for two reasons: (1) spot and 24‐hour urine sodium are frequently used to evaluate salt intake, but are more suitable for population study, and (2) some adverse effects of salt may be blood pressure‐independent. In recent years, previously unknown mechanisms of sodium absorption and storage have been discovered. This review will outline the limitations of current methods to assess sodium balance and discuss new potential evaluation methods and treatment targets.

The Future of Hybrid and Inorganic Perovskite Materials: Technology Forecasting

Perovskite (PSK) materials have received extensive attention because of their potential use as the active layer of different applications, including capacitors, sensors and detectors, memories, catalysts, fuel cells, and optoelectronic devices (solar cell and light‐emitting diode). Herein, the past, current, and future status of these applications are focused on. The state of each application is investigated using diverse technology indicators. It reveals that among all the applications, the optoelectronic application is the youngest one and has not yet reached the technology growth stage though it possesses most of the recently published patents. Using a technology investment potential evaluation method, the most potent area for investment is explored, indicating that detectors and sensors are the ones with the biggest investment potentials. Remarkably, PSK solar cell dedicates more than 70% of the patents to itself with the highest predicted efficiency of 30% that will be achieved in 2023 according to the curve fitting method and in 2030 according to the S‐curve method. As a perspective, currently, the highest efficiency and stability are achieved for small‐sized PSK solar cells, it is expected that they would be commercialized in electronic equipment applications first, and after overcoming the challenges, large‐scale modules would enter the markets.

The total social cost evaluation of two wind and PV energy development modes: A study on Henan of China

In Henan province of China, the power demand is vigorous, and the existing provincial generators are mostly high-carbon. Henan faces greater challenges in electricity revolution than other provinces. Currently, there are two development modes for generating low-carbon electricity in Henan: the “large-scale and long-distance transmission” (LSLDT) mode and the “local development and consumption” (LDC) mode. To make a better choice on development mode, following works have been done. Firstly, as there is still no macro evaluation on the self-sufficiency ratio by photovoltaic (PV) power in each region of Henan, a rooftop PV installation potential evaluation method based on the roof covering rate is proposed. And two development scenes for wind and PV power generation are set to fit the development trend of Henan in the near future. Secondly, as the load can only be stably supplied by the cooperation of wind turbine, PV equipment and other controllable generation, the real cost of wind turbine and PV power generation is never only the equipment investment. Different from the commonly used levelized cost of electricity method, a bi-level generation planning model based on the total social cost is established. Finally, generation planning schemes are made and the power supply cost per kWh electricity is evaluated under the two development scenes mentioned above. It is found that there is a significant cost advantage when wind and PV electricity are generated with the LDC mode in both scenes. What is more, the existence of reliability cost, wheeling cost and grid-loss cost makes the LSLDT mode difficult to cut down the wind and PV power supply cost. Wind and PV power generation of the LDC mode should be focused on in Henan.

The combined use of thermography and ultrasound to evaluate the development of pressure injury in a critically ill patient

Abstract Introduction: Pressure injury is 1 of the most common pressure related injuries in patients admitted to the intensive care unit. In individuals with darker skin tones, skin assessment protocols appear to be less effective, resulting in early damage from pressure. Bedside assessment measures using ultrasound and infrared thermography (IRT) have been studied to identify pressure injuries. Patient concerns: A 58-year-old dark-skinned male was admitted to the intensive care unit due to an ischemic stroke. Diagnosis: The visual evaluation of the skin took place on the second day after admission to the intensive care unit (before 48 hours). The patient had a whitish erythema on the left heel (LH) and a large bloody blister on the right heel. There were no signs of color change on the sacrum skin. Interventions: We performed 3 skin evaluations of the sacrum and calcaneus using ultrasound and IRT. Outcomes: Changes in the temperature of the target regions (sacrum, right heel and LH) were observed. The right heel showed higher mean temperatures than the LH in all evaluations. In the first evaluation of the sacrum region, the average temperature was lower (-1.3°C) than in the second and third evaluation (1°C). In the calcaneus, the mean temperature range (right heel - LH) showed a difference of (3.5°C) in the first evaluation, a difference of (1.4°C) in the second evaluation, and a difference of (1.7°C) in the third evaluation. Ultrasound images of the selected regions showed abnormal tissue patterns - edema - since the first evaluation. Conclusion: These findings indicate that the regions with deep tissue injury on ultrasound evaluation were compatible with the regions of abnormal temperatures in the IRT. IRT could identify regions of pathological process, which could be confirmed by abnormal ultrasound findings. Well-designed, randomized research with a larger sample could verify if the combination of these assessment techniques could be used as a potential method for early detection and evaluation of pressure injuries.

Zero energy potential of photovoltaic direct-driven air conditioners with considering the load flexibility of air conditioners

The real-time energy matching between building load and PV generation is low in actual applications of photovoltaic direct-driven air conditioners (PVACs). The indoor thermal comfort temperature range (TCTR) can enhance the load flexibility of PVACs to improve the real-time zero energy probability. Therefore, a real-time zero-energy potential evaluation method with the adoption of TCTR for PVACs was proposed. The indoor temperatures are mainly determined by real-time PV generation, which minimizes the power taken from the grid and batteries. The indoor temperature, conditioned by PVACs under varying operating conditions, is predicted using machine learning models at a one-minute time resolution. The real-time energy matching performances of PVACs are evaluated by key indicators, including zero-energy probability, real-time zero-energy probability, complete overcooling rate, and complete overheating rate, for different climatic regions. With a fixed indoor setting temperature in summer, the real-time zero-energy probabilities in Shanghai, Guangzhou, and Beijing are 1.89%, 2.45%, and 2.11%, respectively. While adopting the TCTR with the similar PV capacity, the corresponding values in Shanghai, Guangzhou, and Beijing reach 53.13%, 49.48%, and 87.11%, respectively. In addition, the real-time zero-energy points frequently appear when large cooling demands are needed. Finally, an optimization of PV capacity is conducted. The optimal PV capacity was found to be at the point where seasonal PV generation is 1.3 times the dominant seasonal energy consumption. PVACs with TCTR can utilize the PV generation and load flexibility to the greatest extent and the evaluation method for PVACs is beneficial for designing a more practical and economical system.

Frailty Pathogenesis, Assessment, and Management in Older Adults With COVID-19.

The 2019 coronavirus disease (COVID-19) is a highly contagious and deadly disease. The elderly people are often accompanied by chronic inflammation and immunodeficiency, showing a frail state. The strength, endurance, and physiological function of the elderly are significantly decreased, and the ability to deal with stress response is weakened. They are the high-risk group that suffering from COVID-19, and rapidly developing to critical illness. Several recent studies suggest that the incidence rate of COVID-19 in elderly patients with frailty is high. Early assessment, detection, and effective intervention of frailty in COVID-19 patients are conducive to significantly improve the quality of life and improve prognosis. However, there are insufficient understanding and standards for the current evaluation methods, pathogenesis and intervention measures for COVID-19 combined with frailty. This study reviews the progress of the research on the potential pathogenesis, evaluation methods and intervention measures of the elderly COVID-19 patients with frailty, which provides a reference for scientific and reasonable comprehensive diagnosis and treatment in clinical.

Identification of different bran-fried Atractylodis Rhizoma and prediction of atractylodin content based on multivariate data mining combined with intelligent color recognition and near-infrared spectroscopy

Unclear established standard of bran-fried Atractylodis Rhizoma (BFAR), a commonly used drug in Traditional Chinese Medicine (TCM), compromised its clinical efficacy. In this study, we explored the correlation between color and near-infrared spectroscopy (NIR) feature with content of atractylodin, then established a rapid recognition model for the optimal degree of processing for BFAR preparation. The results of the Pearson analysis indicated that the color values were significantly and positively correlated with atractylodin content. The back propagation artificial neural network algorithm and cluster analysis revealed the color of different BFAR could be accurately divided into three categories; subsequently, the color range for the optimal degrees of stir-frying was established as follows: R[red value (105.79–127.25)], G[green value(75.84–89.64)], B[blue value(33.33–42.73)], L[Lightness (81.26–95.09)].Using NIR, principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and cluster analysis, three types of BFAR were accurately identified. The prediction model of atractylodin content was established using partial least squares regression analysis. The R2 of the validation set was 0.9717 and the root mean square error was 0.026. In the color judgment model, the processing degree of 8 batches of BFAR from the market is inferior. According to the NIR judgment model, the processing degree of all samples from the market is inferior. In conclusion, the best fire degree of BFAR can be identified quickly and accurately based on our established model. It is a potential method for quality evaluation of Chinese Materia Medica processing.

Emotional disorder: a neglected symptoms of chronic liver disease

Patients with chronic liver disease generally have emotional disorders that typically manifest as depression, and seriously affects the quality of life. The mechanism of emotional disorders in patients with chronic liver disease is unclear, and may be related to variety of factors such as disease type, etiological treatment, economic, social support, and an individual psychology. Moreover, emotional disorders in patients with chronic liver disease can be assessed on a variety of scales and managed comprehensively through non-drug and drug therapy. This article reviews the potential pathogenesis, evaluation and treatment methods, in order to improve and provide more help for its effective treatment.

Foaming mechanisms and control strategies during the anaerobic digestion of organic waste: A critical review.

Foaming is a problem that affects the efficient and stable operation of the anaerobic digestion process. Characterizing foaming mechanisms and developing early warning and foaming control methods is thus critically important. This review summarizes the correlation of process parameters, state parameters, and microbial communities with foaming in anaerobic digesters; discusses the applicability of the above-mentioned multi-scale parameters and foaming potential evaluation methods for the prediction of foaming risk; and introduces the principles and practical applications of antifoaming and defoaming methods. Multiple causes of foaming in anaerobic digestion systems have been identified, but a generalizable foaming mechanism has yet to be described. Further study of the correlation between extracellular polymeric substances and soluble microbial products and foaming may provide new insights into foaming mechanisms. Monitoring the foaming potential (including the volume expansion potential) is an effective approach for estimating the risk of foaming. An in-situ monitoring system for determining the foaming potential in anaerobic digestion sites could provide an early warning of foaming risk. Antifoaming methods based on operating parameter management and process regulation help prevent foaming from the source, and biological defoaming methods are highly targeted and efficient, which is a promising research direction. Clarifying foaming mechanisms will aid the development of active antifoaming methods and efficient biological defoaming methods.

A new geographical information system approach based on best worst method and analytic hierarchy process for site suitability and technical potential evaluation for large-scale CSP on-grid plant: An application for Algeria territory

For future energy planning and sustainable development, the share of renewable energy in the national power production must be increased. For this reason, energy managers and deciders would impose models and strategies for future renewable energy planning development. The site selection of new renewable energy projects constitutes a huge challenge, due to the contributions of several factors in the decision-making process. This study has two main objectives, the first is to develop an alternative framework for site selection of large-scale parabolic trough concentrating solar power on-grid plants as an essential future solar power in Algeria. A Geographic Information System (GIS) based analysis combined with Multi Criteria Decision Making techniques to develop a high-resolution map that identify and classify the suitable locations for setting up these projects. Several criteria are used in GIS analysis process, a map of direct normal solar irradiation and sunshine duration were developed with high resolution of (92×92 m/pixel) and grid power map was presented in its updatable form. The prioritization degree of the available siting zones is presented through three scenarios where distinct opinions of different energy related groups are considered by using different weighting methods; EQual Weighted (EQW), Analytic Hierarchy Process(AHP) and Best Worst Method (BWM).Second, the paper assesses the theoretical and technical potential of energy generation using concentrating solar power plants. A sensitivity analysis is applied for the suitable lands and finally, the results show that approximately 11% of the study area is considered as available lands for concentrating solar power energy generation with yearly electricity generation 34,453 TWh. The provinces of Bechar, Naama, Elbayadh, Laghouat, Gherdaïa, and Ouargla presenting the large lands of most suitable class (approximately 15,384.73 km2 and 15,986.34 km2 in AHP and BWM scenario respectively. Where the energy generation can reach more than 2,100 TWh/year, which can cover 38 time the national electricity demand.

Energy-based liquefaction evaluation for induced strain and surface settlement ― evaluation steps and case studies ―

Energy-Based Method (EBM) for liquefaction potential evaluation was previously developed by the present author, wherein the total demand compared with the capacity at a site can be given by earthquake upward SH-wave energy. To make the most of the EBM with the energy demand given, evaluations are added here to calculate the liquefaction-induced strain and associated soil settlement by introducing a simple assumption that the wave energy is equally shared among layers firstly determined as liquefiable. By applying this to a uniform sand layer as well as case history sites, the additional liquefaction evaluation tends to calculate much higher shear strains than 7.5% corresponding to initial liquefaction in a smaller number of layers than in the first stage. Furthermore, soil subsidence calculated using volumetric strain versus shear strain correlations for intact soils is found agreeable with actual performance during earthquakes. Thus, the two-stage EBM of liquefaction evaluation can practically predict not only liquefaction potential but also induced strain and surface settlement without resorting to nonlinear numerical analyses.

Method for Potential Evaluation and Parameter Optimization for CO2-WAG in Low Permeability Reservoirs based on Machine Learning

The CO2 water-alternating-gas flooding (CO2-WAG) is a key technology to improve the oil recovery of low permeability reservoirs. The effect of CO2 flooding to enhance the oil recovery is affected by geological conditions and production systems. The effect of CO2 flooding parameters on the enhanced recovery factor should be clarified to optimize the production system. In this paper, the machine learning algorithms are used to carry out the study and establish a set of procedures for optimizing CO2 flooding parameters based on the artificial neural network (ANN) and the particle swarm optimization (PSO) algorithm. Firstly, large amounts of basic data are generated by the Monte Carlo sampling method. Then, the recovery factor by the water flooding and the CO2-WAG and the enhanced recovery factor by CO2-WAG in different models are calculated in the reservoir numerical simulator. Moreover, the machine learning method is used to establish a neural network model, and analysis of the sensitivity of parameters of the enhanced oil recovery (EOR) is carried out by combining with the Sobol method. Finally, the neural network model and the particle swarm algorithm are combined to optimize the parameters of CO2-WAG flooding. The results show that the established model has a good prediction accuracy (97.6%), thus it could be used to predict the enhanced recovery factor by CO2-WAG, and it is applicable in potential evaluation of enhancing the oil recovery and optimization for parameters in the CO2-WAG well group.

Development Favorable Area and Productivity Potential Evaluation Method of a Tight Oil Reservoir

Tight oil resources have become the focus of unconventional oil and gas exploration and development. Well placement is an essential factor determining the development of a field. Oil wells should be located in the area with favorable criteria for development. These areas should be screened based upon oil and gas enrichment of the reservoir. The influencing factors and analysis theory of an enrichment area are summarized in this paper. Two types of methods evaluating areas favorable for tight oil and gas production are explained here as well, including model prediction and the combination of geological modeling and reservoir simulation. The area with favorable geological, engineering, and economic attributes has the best development potential. The productivity potential can be used as one basis for selecting areas favorable for production. Based on the previous concept of productivity potential, combined with the characteristics of a tight oil reservoir, this study modified the evaluation of productivity potential, and the calculated potential area was the priority for well placement. The modified equation of productivity potential comprehensively considers effective pore pressure, mobile oil saturation, porosity, permeability, effective thickness, distance from the boundary, and threshold pressure gradient. A tight reservoir was taken as an example for calculation, and the results of the modified method, original productivity potential method, and reserve abundance calculation method are discussed. Two new wells were arranged in the favorable areas obtained by different productivity potential evaluation methods, and the production was calculated under the same parameters for each method. The recovery of this area was 51.65%, which is 1.73% and 2.84% higher than that of the other two methods.

Hydrocarbon source potential evaluation insight into source rocks—A case study of the first member of the Paleogene Shahejie Formation, Nanpu Sag, NE China

The features of source rock hydrocarbon generation, expulsion, and retention are essential factors in petroleum resource potential evaluation. In this study, the improved hydrocarbon generation potential evaluation method was used to quantify the characteristics of the source rocks of the first member of the Paleogene Shahejie Formation (Es1) in the Nanpu Sag, Bohai Bay Basin, NE China. Based on the geological and geochemical data for the source rocks, the favorable exploration areas were predicted and the resource potential was evaluated. Our results show that Es1 is a set of high-quality source rocks with desirable geological (maximum thickness > 600 m) and geochemical (average total organic carbon content of 1.44%; dominantly type II kerogen; mature stage) characteristics. The improved hydrocarbon generation potential model shows that the hydrocarbon expulsion threshold is located at Ro=1.0%. The maximum hydrocarbon generation potential (Qg), hydrocarbon expulsion potential (Qe), hydrocarbon retention potential (Qs), and efficient hydrocarbon retention potential (Qse) are 549.31, 162.20, 387.11, and 287.11 mg HC/g TOC, respectively. The maximum hydrocarbon generation, expulsion, retention, and effective retention intensities are 250×104, 70×104, 180×104, and 150×104 t/km2, respectively. These parameters reach the highest value in the Linque and Caofeidian Sub-sag. The resource potential of the retention shale oil, the movable shale oil, and the conventional and the tight petroleum resources are 1.99×108, 4.38×108, and 1.37×108 t oil equivalent, respectively. This study offers a fresh and plain but operable workflow for resource evaluation, which can be applied in other petroliferous basins with low degree of exploration.

A New Method for Potential Evaluation of Compound Flooding of Tertiary Oil Recovery

Compound flooding and tertiary oil recovery is one of the leading methods to improve recovery efficiency in oil and gas development in China, Because there are certain requirements on the exploitation environment and technology in the process of petroleum exploitation, it is necessary to evaluate and analyze the reservoir potential in advance. Currently, our research team has integrated this method into the tertiary oil production potential evaluation software EORSYS3.0. In this paper, a new combined flooding potential prediction model is established to improve the accuracy of reservoir potential prediction results by changing the reservoir water cut of displacement front viscosity.

Possible Assessment Method of Green Infrastructure in the Case of Small Town Keszthely, Hungary

Recently the planning of green infrastructure (GI) has become a general practice around metropolis (Paris, München, Budapest, etc). A complex methodology is required that goes beyond the scope of traditional green surface systems. However, there are various policy implications in the EU, the smaller towns are lagging to apply them. The paper presents a potential evaluation method through the case study of Keszthely, HU. As Keszthely at Balaton Riviera, is a popular touristic target of CEE, the environmental planning is an essential part of sustainable development. 
 After a literature analyses of assessment methods of GI and ecosystem services, the aspects of GI have been valued on grade scales, based on field surveys and indicators. The current status of the GI was surveyed which is a base for further development and monitoring activities. The paper introduces the methodology, which contributes to preservation of ecosystems.

Assessment method of comprehensive energy saving potential of distribution network considering source-load power uncertainty

Abstract The comprehensive energy-saving potential evaluation method of the energy-saving schemes of a distribution network considering the power uncertainty of source and load is studied in this paper. The K-means clustering method is firstly employed to extract typical scenarios of distribution network, and the forward-push back method is used to calculate the power flow in typical scenarios, then energy-saving reconstruction schemes are formulated according to the power flow calculation results. A comprehensive energy saving potential evaluation index system that consists the improvement rate of network loss, line loss rate, transformer loss rate, annual electricity saving cost, annual equipment investment cost, annual maintenance cost and voltage quality improvement rate is built, and the comprehensive evaluation method based on DEMATEL-ANP-TOPSIS mixed decision model is used to evaluate the comprehensive energy-saving potential of the energy-saving reconstruction schemes regarding the index system. Finally, the optimal reconstruction scheme is selected based on the evaluation results of energy saving potential in multiple scenarios. The effectiveness of the proposed method is verified by an example in IEEE-33 node distribution network.

Liquefaction potential evaluation for iron ore fines based on a correlation between moisture content and cyclic strength

Iron ore fines (IOFs) are indispensable material for industries with over million tons transported annually by sea. The transportation involves serious risk of marine accidents due to cargo liquefaction caused by the loss of shear strength under cyclic loads. In this work, the effect of moisture content on the unsaturated shear strength of the IOFs was analyzed, including the matric suction and the effective normal stress. The cyclic strength of the IOFs as a function of moisture content and number of cycles was presented, as well as a correlation between the cyclic strength ratios in the field and laboratory tests. Moreover, dynamic geotechnical centrifuge model tests were conducted to mimic the field condition and verify the method for liquefaction potential evaluation of the IOFs. The results showed that moisture content was the critical factor affecting the shear strength of the IOFs. The zones with high liquefaction potential were identified according to the cyclic strength ratio and the cyclic shear stress ratio along the depth. The evaluation results agreed well with those from the dynamic geotechnical centrifuge model tests. This work provided an approach to accurately evaluate the liquefaction potential of the IOFs utilizing the variation of moisture content during shipping.

Logistics potential usage for railway transport enterprises competitivness assessment

Research relevance. The problem of logistics potential development for the railway transportation companies with a purpose of strengthening its competitive positions is actualized in the conditions of railway transportation market liberalization and deregulation. Purpose: to develop a methodology for railway enterprises logistics potential evaluation by supplementing it with qualitative parameters (by applying the hierarchy analysis model) and thus expanding its scope. In particular, when assessing competitiveness. Methods: the hierarchy analysis model by T. Saati; expert evaluation method; the integral index of competitiveness evaluation method. Conclusions and value added: The enterprises logistics potential evaluation method with hierarchy analysis model usage was further developed. The logistics potential assessment problem is presented in a form of three-tier hierarchy of criteria. The hierarchy consists of eighteen third-level criteria and five second-level criteria, which compose a comprehensive system of indicators for railway enterprise logistics potential assessing. The qualitative parameters were obtained for enterprises logistics potential criteria evaluation. The research with the described method usage allowed to distinguish the components of logistical potential according to their level of importance in terms of strategic priorities for the industry development in general and the target market requirements; and to identify those criteria, the consideration of which will allow to increase the railway transport enterprises competitiveness. This approach expands the enterprise’s logistics potential methodology scope, especially when assessing competitiveness, helps to choose a further strategic direction.