Production Data Research Articles

Long-term performance analysis of operational efficiency of a grid-connected solar power plant under Mauritania climate

This work examines a solar power plant connected to the Nouakchott electricity grid in Mauritania. Operating since 2013, the 15 MWp plant's reliability and energy yield have been evaluated using a performance index. The assessment involves three phases. First, the plant's meteorological environment and technical indicators are presented. In the second phase, mathematical performance models specified by the International Energy Agency (IEA) are applied to calculate performance indices using data from the data acquisition system (SCADA). The third phase compares actual production data for 2015, 2017, and 2020 with results simulated for PVsyst for the same years. The obtained results are thoroughly analyzed to highlight relevant physical phenomena. The analysis focuses on the plant's 7-year operating period and its impact on performance indicators for electricity production fed into the grid. This study provides insights into the solar power plant's reliability and energy yield, aiding future operational enhancements. It underscores the importance of performance monitoring and assessment in optimizing solar power generation systems.

Reduction of rejects by combining data from the casting process and automatic X-ray inspection

Automatic inspection of castings with X-rays (radiographic and computed tomography) is widespread for parts that are relevant for safety or have high quality requirements. Examples in the automotive sector are aluminum wheels, chassis parts and new parts within the electric power train. Those parts are automatically inspected, which means that both the image acquisition and the evaluation of the images is done fully automatically. Today, in most industrial implementations, the generated data with a size up to several gigabytes per part is summarized to a simple good or bad decision, according to specification. All other data is dismissed, although this information can be valuable to optimize production processes and thus minimize rejects. This contribution gives an overview about the results of the project Cast Control, which is a collaboration of Fraunhofer Development Center for X-ray Technology EZRT, Fraunhofer Center for Applied Research on Supply Chain Services SCS and industry partner RONAL GROUP. RONAL GROUP is a major aluminum wheel manufacturer, mainly for the OEM market. Within the project we combined serial production data from the low pressure die casting process from a foundry of the RONAL GROUP with the data generated in the automatic X- ray inspection. After collecting a large base of sample data, we were able train a neural network for the prediction of error metrics obtained by X-ray inspection. We apply a combination of layer-wise relevance propagation and dimensionality reduction to find correlations between data of the casting machines (process and sensor) and the characteristics of anomalies detected during X-ray inspection. With this information, it is possible to adjust the casting process in an early stage – even before rejects are produced. This enables the foundry to reduce their rejects rate, which saves costs and energy and results in a better competitiveness.

MyPharmaceutical: an interactive proof of concept

With the rise of health awareness, pharmaceutical and cosmetic products should be verified to protect ourselves from health risks. MyPharmaceutical is a proof-of-concept proposed to provide a mobile application for users to carry out product verification and reporting and a web application for administrative purposes. The data on the registered pharmaceutical and cosmetic products were extracted from national pharmaceutical regulatory agency (NPRA) website. MyPharmaceutical mobile application provides functionalities such as searching the registered product, bookmarking products, reporting products, and tracking report status. The mobile application also implemented a barcode scanner feature to provide ease of product verification. A named entity recognition algorithm is applied with the NLP.js library to provide an improved product search feature for the users, where products can be searched with multiple search criteria in a single input. The web application is proposed to support the mobile application, where the NPRA data admins and officers can manage reported products, publish announcements, verify product data, and utilize the analytic dashboard. The system proposed is expected to provide ease of product verification and reporting to assist the public in choosing safe registered products and a platform for NPRA to manage data and deliver information to the users.

Improving value chain data lifecycle management utilising design for Lean Six Sigma methods

PurposeThis project aims to optimise a secondary agricultural company’s reporting and data lifecycle by providing self-help business intelligence at an optimal price point for all business users.Design/methodology/approachA design for Lean Six Sigma approach utilising the define, measure analyse, design and verify methodology was utilised to design a new reporting and data product lifecycle.FindingsThe study found that this approach allowed a very structured delivery of a complex program. The various tools used assisted greatly in delivering results while balancing the needs of the team.Practical implicationsThis study demonstrates how improving data analysis and enhanced intelligence reporting in agribusinesses enable better decision making and thus improves efficiencies so that the agribusiness can leverage the learnings.Social implicationsImproving data analysis increases efficiency and reduces agrifood food wastage thus improving sustainability and environmental impacts.Originality/valueThis paper proposes creating a standardised approach to deploying Six Sigma methodology to correct both the data provisioning lifecycle and the subsequent business intelligence reporting lifecycle. It is the first study to look at process optimisation across the agricultural industry’s entire data and business intelligence lifecycle.

Bayesian quantile inference and order shrinkage for hysteretic quantile autoregressive models

Hysteretic quantile autoregressive model combines the hysteretic patterns and quantile autoregression, which can capture the dynamic relationship and nonlinear characteristics at different quantiles in time series data. In this paper, the Bayesian quantile inference and order shrinkage are studied for a class of hysteretic quantile autoregressive time series models. By using Markov Chain Monte Carlo (MCMC) techniques, the proposed Bayesian quantile method can handle the sparse hysteretic quantile autoregressive model well. It can accurately determine order of the model and estimate non-zero coefficients. Both simulation studies and a data example show that the proposed methods are feasible, reliable and appropriate for analysing the US Gross National Product data set.

The Optuna–LightGBM–XGBoost Model: A Novel Approach for Estimating Carbon Emissions Based on the Electricity–Carbon Nexus

With the challenge posed by global warming, accurately estimating and managing carbon emissions becomes a key step for businesses, especially power generation companies, to reduce their environmental impact. Optuna–LightGBM–XGBoost, a novel power and carbon emission relationship model that aims to improve the efficiency of carbon emission monitoring and estimation for power generation companies, is proposed in this paper. Deeply exploring the intrinsic link between power production data and carbon emissions, this model paves a new path for “measuring carbon through electricity”, in contrast to the emission factor method commonly used in China. Unit data from power generation companies are processed into structured tabular data, and a parallel processing framework is constructed with LightGBM and XGBoost, and optimized with the Optuna algorithm. The multilayer perceptron (MLP) is used to fuse features to enhance prediction accuracy by capturing characters that the individual models cannot detect. Simulation results show that Optuna–LightGBM–XGBoost can achieve better performance compared to existing methods. The mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) of the model are 0.652, 0.939, 0.136, and 0.994, respectively. This not only helps governments and enterprises to develop more scientific and reasonable emission reduction strategies and policies, but also lays a solid foundation for achieving global carbon neutrality goals.

Matching of Water Breakthroughs in a Low-Resistivity Oil Reservoir Using Permeability Anisotropy

In a mature middle and lower Gharif field in Oman, uncertainties surrounding initial water saturation and early water breakthroughs of unknown sources and paths suggest the presence of significant bypassed oil. In order to determine the areas with remaining oil, petrophysical and logging data of seven wells were processed using Techlog software and imported into Petrel software for modelling and simulation. Porosity was calculated using the Electric Propagation Time log and was utilized to evaluate the presence of oil, particularly in the upper tight zone of the formation. Despite the low resistivity readings in the highly porous layers, caused by good network connectivity and high formation water salinity, the resistivity contrast was sufficient to differentiate them from the oil zone. However, the calculated water saturation (Sw) in the tight top oil zone was high, consistent with the observed water production in the field. To improve the match between production data and simulation results, sensitivity analyses were conducted on various permeability anisotropy and relative permeability values within the model. The analyses showed that core-derived permeability anisotropy (vertical to horizontal ratio of 1:1) yielded a better history match for water production compared to the conventionally used value of 1:10. Water saturation maps were generated at the start and the end of production to highlight saturation distribution within the reservoirs. The maps revealed that in the lower porous part, the oil was fully depleted around the wells but remained trapped in the undrilled areas.

Measuring persistent global economic factors with output, commodity price, and commodity currency data

AbstractIn this study, we use monthly G7 industrial production data, commodity price index data, and commodity currency exchange rate data in a dynamic factor model to examine the global economic factors useful for commodity price prediction. We differentiate between the dynamic factors by specifying a persistent factor and a non‐persistent factor, both as a single global factor using all data and as factors for each category of data. The in‐sample predictive performances of the three persistent factors together are better than the non‐persistent factors and the single global factors. Out‐of‐sample outcomes based on forecast combinations also support the presence of predictive information in the persistent factors for overall commodity prices and for most sub‐categories of commodity price indexes relative to their means. The gains in forecast accuracy are heterogeneous, ranging from 5% to 7% in the 1‐ to 6‐month horizon for overall commodity prices to a high of around 20% for fertilizers in the 12‐month horizon in the recent sample. We further show that the information in the persistent factors, especially in the commodity currency exchange rate‐based persistent factor, can be integrated with other global measures to further improve the predictive performances of the global measures.

Comparative analysis of spatial transport characteristics of spring cold-front-type sandstorms in the Hexi Corridor

AbstractIn this study, we investigate the spatial transport characteristics of cold-front-type sandstorms with different intensities in the Hexi Corridor based on the hourly observation data from 13 ground-based meteorological stations in the Hexi Corridor, the data in the upper and lower layers from the Meteorological Information Comprehensive Analysis And Process System, and the three-dimensional aerosol observation data of the Vertical Feature Mask product obtained from the Cloud-Aerosol Lidar with Orthogonal Polarization of the United States. The results show that the influence range and horizontal transport distance of cold-front-type sandstorms in the Hexi Corridor are determined by the intensity, thickness and width of the upper-level troughs and the intensity of cold fronts. Generally, cold fronts pass through the Hexi Corridor from noon to evening and stay in this region for a long time or move slowly, which is favorable to the horizontal long-distance transport of sandstorms. The intensity of the 200 hPa upper-level jets is directly proportional to both the influence range and transport distance of sandstorms which primarily occur on the left side of the exit area of an upper-level jet. The strengthening of upper-level jets induces the formation of the vertical circulation cell at middle and low levels. The ascending branch of the circulation cell lifts sand-dust particles from the surface to the upper layers, and the descending branch transports upper-level momentum to the near-surface. This circulation situation increases the near-surface wind speed and forms sandstorms. The occurrence time and descending branch of the secondary circulation cell caused by upper-level jets are important indicators for predicting the occurrence time and falling area of sandstorms. Dust aerosols are mainly concentrated at a height of 2–6 km,.,and they can reach the height of 12 km. When the dust is concentrated at the height of 2–8 km, and the wind speed of 500 hPa and 300 hPa reaches 28 and 32 m s− 1 or above respectively, the cold front sandstorm can realize the long distance transport of thousands of kilometers.

APPLICATION OF ARIMA MODEL FOR FORECASTING NATIONAL ECONOMIC GROWTH: A FOCUS ON GROSS DOMESTIC PRODUCT DATA

This study aims to apply the Autoregressive Integrated Moving Average (ARIMA) model to predict national economic growth, specifically focusing on Gross Domestic Product (GDP) data. GDP data were collected from 2012 to 2023, categorized into training data for the period 2012-2022 and testing data for the year 2023. Utilizing the training data, the research findings indicate that the ARIMA (0,1,0) (0,0,1) model emerges as the most effective in forecasting Indonesia's GDP on a quarterly basis, considering current prices. Subsequently, the model was tested on the 2023 dataset, and it demonstrated accurate predictions aligned with patterns and trends identified during the training phase. The outcomes of this research contribute significantly to the field of economic forecasting in Indonesia, particularly in understanding and predicting the quarterly developments of GDP. The proposed ARIMA model can serve as an effective tool for decision-makers and economic analysts to strategically plan for future economic dynamics on a quarterly basis.

A Capillary-Force-Driven, Single-Cell Transfer Method for Studying Rare Cells

The characterization of individual cells within heterogeneous populations (e.g., rare tumor cells in healthy blood cells) has a great impact on biomedical research. To investigate the properties of these specific cells, such as genetic biomarkers and/or phenotypic characteristics, methods are often developed for isolating rare cells among a large number of background cells before studying their genetic makeup and others. Prior to using real-world samples, these methods are often evaluated and validated by spiking cells of interest (e.g., tumor cells) into a sample matrix (e.g., healthy blood) as model samples. However, spiking tumor cells at extremely low concentrations is challenging in a standard laboratory setting. People often circumvent the problem by diluting a solution of high-concentration cells, but the concentration becomes inaccurate after series dilution due to the fact that a cell suspension solution can be inhomogeneous, especially when the cell concentration is very low. We report on an alternative method for low-cost, accurate, and reproducible low-concentration cell spiking without the use of external pumping systems. By inducing a capillary force from sudden pressure drops, a small portion of the cellular membrane was aspirated into the reservoir tip, allowing for non-destructive single-cell transfer. We investigated the surface membrane tensions induced by cellular aspiration and studied a range of tip/tumor cell diameter combinations, ensuring that our method does not affect cell viability. In addition, we performed single-cell capture and transfer control experiments using human acute lymphoblastic leukemia cells (CCRF-CEM) to develop calibrated data for the general production of low-concentration samples. Finally, we performed affinity-based tumor cell isolation using this method to generate accurate concentrations ranging from 1 to 15 cells/mL.

Larger nations benefit more than smaller nations from the stabilizing effects of crop diversity.

Crop diversification is increasingly recognized as a strategy to stabilize national food production, yet the benefits of this approach may vary across nations due to the scale dependence of crop diversity and stability. Here we use crop production data from 131 nations from 1961 to 2020 to explore the spatial scale dependence of the crop diversity-stability relationship. Drawing on ecological theory and complementary analytical approaches, we find that as the total national harvested area increases, yield stability increases. Crop diversity stabilizes national yield stability, as does an increase in the number of farms, but these stabilizing effects are weaker in smaller countries. Our findings suggest that enhancing crop diversity at the national level may not provide a de facto universal strategy for increasing yield stability across all countries-with implications for national strategies promoting crop diversification to protect against food system shocks.

Monitoring of Extreme Drought in the Yangtze River Basin in 2022 Based on Multi-Source Remote Sensing Data

The Yangtze River Basin experienced a once-in-a-century extreme drought in 2022 due to extreme weather, which had a serious impact on the local agricultural production and ecological environment. In order to investigate the spatial distribution and occurrence of the extreme drought events, this study used multi-source remote sensing data to monitor the extreme drought events in the Yangtze River Basin in 2022. In this study, the gravity satellite data product CSR_Mascon was used to calculate the GRACE Drought Intensity Index (GRACE-DSI), which was analyzed and compared with the commonly used meteorological drought indices, relative soil humidity, and soil water content data. The results show that (1) terrestrial water storage change data can well reflect the change in water storage in the Yangtze River Basin. Throughout the year, the average change in terrestrial water storage in the Yangtze River Basin from January to June is higher than the average value of 33.47 mm, and the average from July to December is lower than the average value of 48.17 mm; (2) the GRACE-DSI responded well to the intensity and spatial distribution of drought events in the Yangtze River Basin region in 2022. From the point of view of drought area, the Yangtze River Basin showed a trend of extreme drought increasing first, and then decreasing in the area of different levels of drought, and the range of drought reached a maximum in September with a drought area of 175.87 km2, which accounted for 97.71 per cent of the total area; at the same time, the area of extreme drought was the largest, with an area of 85.69 km2; (3) the spatial and temporal variations of the GRACE-DSI and commonly used meteorological drought indices were well correlated, with correlation coefficients above 0.750, among which the correlation coefficient of the SPEI-3 was higher at 0.937; (4) the soil moisture and soil relative humidity products from the CLDAS, combined with soil moisture products from the GLDAS, reflect the starting and ending times of extreme drought events in the Yangtze River Basin in 2022 well, using the information from the actual stations. In conclusion, gravity satellite data, analyzed in synergy with data from multiple sources, help decision makers to better understand and respond to drought.

Organic residue analysis in Latin American archaeology: Past, present, and future perspectives

AbstractOrganic residue analysis (ORA) studies residual anthropogenic organic molecules within archaeological matrices, offering valuable insights into various research inquiries. Despite the development of ORA in Latin America, its significance remains largely unrecognised and underrepresented. This paper aims to provide a comprehensive overview of the evolution, status, and prospects of ORA in Latin America. To achieve this, we conducted a thorough bibliographic survey and analysed contributions related to topics such as foodways, psychoactive consumption, the utilisation of paints, dyes, adhesives, binders, activity areas, and residue radiocarbon dating. We examine the geographical distribution of the studies, the flow between countries of sample origin and destination for ORA, and the chronological development in publications. We address challenges encountered in the production of ORA data in Latin America and emphasise the need for multiproxy and multi‐analytical approaches to characterise and identify robust biomarker sets to investigate the use and circulation of local commodities.

A cost-benefit analysis of WildFireSat, a wildfire monitoring satellite mission for Canada.

In anticipation of growing wildfire management challenges, the Canadian government is investing in WildFireSat, an Earth observation satellite mission designed to collect data in support of Canadian wildfire management. Although costs of the mission can be reasonably estimated, the benefits of such an investment are unknown. Here we forecast the possible benefits of WildFireSat via an avoided cost approach. We consider five socio-economic components: suppression costs (fixed and variable), timber losses, property, asset and infrastructure losses, evacuation costs, and smoke related health costs. Using a Monte Carlo analysis, we evaluated a range of possible changes to these components based on expert opinions. The resulting Net Present Value (NPV) estimates depend on the presumed impact of using WildFireSat decision support data products, with pessimistic and conservative assumptions generating mission costs that typically exceed potential benefits by 1.16 to 1.59 times, while more optimistic assumptions generate benefits in excess of costs by 8.72 to 10.48 times. The analysis here excludes some possibly significant market and non-market impacts expected from WildFireSat due to data limitations; accounting for these additional impacts would likely generate positive NPVs under even cautious impact assumptions.

Preface to the special issue “EarthCARE Level 2 algorithms and data products”: Editorial in memory of Tobias Wehr

Preface to the special issue “EarthCARE Level 2 algorithms and data products”: Editorial in memory of Tobias Wehr

BRAIN - Holocene archaeo-data for assessing plant-cultural diversity in Italy and other Mediterranean regions

In the field of botany applied to archaeological and palaeoecological studies, the multi- and inter-disciplinary nature of this research produces a lack of data sharing and scattered articles in the specialty literature or in national and international journals. The vast production of archaeobotany and palynology data makes it necessary to develop a tool for the availability, accessibility, and dissemination of existing research. Many databases exist on palaeoecology, archaeobotany or pollen data. There are no collections focused on archaeological sites and human-induced environments and centred on Southern Europe and the Mediterranean. BRAIN - Botanical Records of Archaeobotany Italian Network is the first database listing sites from which all types of plant records are available in Italy and nearby Mediterranean regions. BRAIN represents the largest integrated collection of archaeo/palaeo-botanical data and a range of descriptive information that makes data recovery FAIR ready. This unique network hosts data on the availability of anthropogenic pollen, palynomorphs and plant macroremains in the same database, and experts of different research fields may contribute to it.

Benchmarking study of coal seam gas production from Brownfield to Greenfield wells in the Surat basin, Australia

Benchmarking coal seam gas (CSG) production from Brownfield to Greenfield is an important step for assessing the development plan for Greenfield. However, the observed Brownfield production data is impacted by many factors besides reservoir properties, e.g., well spacing, well efficiency factor (WEFAC), surface pipeline network. This paper presents a case study in the Surat Basin to discuss how to benchmark CSG production from Brownfield to Greenfield. The selected Brownfield study area includes 143 wells and the Greenfield 450 proposed wells. Various tools for static modelling, decline curve analysis, and dynamic modelling were used in this study. Results show that the peak gas rate decreases quickly and the time to peak gas rate increases quickly when the WEFAC is less than 0.65. The peak gas rate is reached when the cumulative water production is between 39% and 46% of water-in-place for the selected box model. After introducing the wells’ online time ratio or WEFAC from the daily production data and the ratio of the observed cumulative water production to the expected cumulative water production, gas production rates can be predicted based on the modified Morse potential energy equation. Artificial neural networks (ANN) can be used to estimate the peak gas rate, the time to peak gas rate and the decline rate based on the reservoir properties; while the ramp-up rate can be calculated from the time to peak gas rate.

Using AI to Empower Norwegian Agriculture: Attention-Based Multiple-Instance Learning Implementation

Agricultural development is one of the most essential needs worldwide. In Norway, the primary foundation of grain production is based on geological and biological features. Existing research is limited to regional-scale yield predictions using artificial intelligence (AI) models, which provide a holistic overview of crop growth. In this paper, the authors propose detecting several field-scale crop types and use this analysis to predict yield production early in the growing season. In this study, the authors utilise a multi-temporal satellite image, meteorological, geographical, and grain production data corpus. The authors extract relevant vegetation indices from satellite images. Furthermore, the authors use field-area-specific features to build a field-based crop type classification model. The proposed model, consisting of a time-distributed network and a gated recurrent unit, can efficiently classify crop types with an accuracy of 70%. In addition, the authors justified that the attention-based multiple-instance learning models could learn semi-labelled agricultural data, and thus, allow realistic early in-season predictions for farmers.

Persistence of fisheries production: a disaggregated analysis in 31 OECD Countries

Previous studies have focussed on different aspects of fisheries production in several countries. However, the literature has been largely silent on the persistence of fisheries production, which has numerous important policy implications. In this article, we examine time series data corresponding to fisheries production in a group of 31 OECD countries. The analysis focuses on three distinct production series for each country: total fisheries production, aquaculture production, and capture fisheries production, and the time period spans from 1960 to 2020. Using fractional integration methods, the empirical findings provide evidence for persistence in fisheries production and its two components in the majority of the OECD countries examined. Persistence is observed in 29 countries in the case of the total fisheries production, 28 countries in the case of capture fisheries production, and 28 countries in the case of aquaculture production. Evidence of mean reversion is observed in Chile and Colombia for the total fisheries production, and in Belgium, Colombia, and Israel for the capture fisheries production. There is support for mean reversion in aquaculture production data for France, the Netherlands and Poland. An implication of the results is that shocks to the fisheries production or its components are permanent or long-term in nature. Therefore, long-term measures are need to address any disruptions in the fishery sector.