Performance Of Production System Research Articles

Conversion to organic farming: Does it change the economic and environmental performance of fruit farms?

This paper compares the performance of conventional and organic fruit farms in Spain, using a set of base indicators to assess their economic and environmental performance on a per hectare basis. Composite indicators are also calculated to measure the overall economic and environmental performance of both production systems. Comparisons are made using propensity score matching to minimize the non-randomization biases caused by structural differences between the samples of conventional (n = 552) and organic (n = 127) fruit farms sourced from the Spanish Farm Accountancy Data Network (RECAN). The results based on per hectare metrics point to modest changes in the performance of farms that converted to organic farming. This is mainly because most converted farms were former conventional farms characterized by lower profitability and less intensive input use, consequently minimizing the effects of the conversion process. Nevertheless, the conversion outcomes exhibit a degree of variability depending on the specific type of fruit production. Economic gains are only discernible in the case of nut farms, whereas fruit and tropical fruit farms tend to yield the most favorable results per hectare from an environmental perspective.

Improving the performance of a condensation water production system through support vector machine modeling and genetic algorithm optimization

Abstract Water scarcity is recognized as a critical global concern and one viable solution involves extracting water from atmospheric humidity by leveraging subterranean coldness. This study meticulously evaluates the operational efficacy of a water production system by examining four pivotal factors: buried pipe length (TL), air flow rate (AFR), air temperature (AT), and air humidity (AH). A positive correlation between these variables and water vapor production is established, with AT exerting the most pronounced influence. Significantly, the analysis of variance reveals the main and interactive effects of the variables, except for TL × AFR, at a 5% significance level. To enhance understanding of the intricate interplay among these factors, a proficient least squares support vector machines model is devised, employing a radial basis function kernel. This model demonstrates an impressive 98% concurrence between projected and empirical data, with a minimal error of 0.66 mL and 5.99%. An in-depth sensitivity analysis underscores the differential impact of AT, AH, TL, and AFR on water vapor (WV) prediction. Optimal values of 3.98 m, 6.89 m3/h, 46.30 °C, and 86.62% for TL, AFR, AT, and AH, respectively, are obtained through subsequent optimization of independent variables using genetic algorithms, resulting in a notable water production of 23.61 mL.

Tall fescue tiller survival over summer in a subtropical environment: The role of the size and depth of root systems

AbstractPasture persistence is a key determinant of the economic and environmental performance of pastoral animal production systems. Large and deep root systems that help resist summer water stress have been proposed as a relevant trait for vegetative persistence of perennial temperate forage species growing in subtropical climates or under future climatically challenging scenarios. In a previous study [Jauregui et al., 2017. Persistence of tall fescue in a subtropical environment: Tiller survival over summer in response to flowering control and nitrogen supply. Grass and Forage Science 72, 454–466] we have shown that nitrogen fertilization and grazing management aimed at ‘control flowering’ increased the survival of tall fescue tillers during harsh summers in Uruguay (lat. 32°S). Here we assessed: (i) to what extent tiller survival is mediated by root system size in spring and (ii) what consequences tiller survival entails for root mass, depth and morphology the following autumn. In two field experiments, significant increases in tiller survival in response to nitrogen fertilization and grazing management (+60% and +80% in 2011/12 and 2012/13, respectively) were not related to concomitant effects on the size or depth of the root system in spring (p > .10). Even when six‐fold within‐treatment variation in root mass was observed, within‐treatment variation in summer tiller survival was little affected (<15%, p = .08). In turn, differences in tiller survival over summer affected little root system characteristics the following autumn. Therefore, we found scant support for the hypothesis that large and deep root systems contribute to survival of tall fescue tillers in this subtropical humid climate. Except for soils with less than 30 mm of plant available water holding capacity, summer water deficits did not induce severe tiller mortality in tall fescue in this climate.

Influência de suplementos de baixo e médio consumo na fase de crescimento de novilhos mantidos em pastagem tropical e terminados em confinamento

ABSTRACT: The objective was to evaluate the animal production and ingestive behavior of the steers fed different supplements during the growth phase in Aruana grass, and the influence of this supplementation on production performance in feedlot phase. The treatments consisted of three supplements during the growing phase: mineral salt (0.12 g/kg of body weight - BW), low intake energy-protein supplement (1.26 g/kg of BW), and medium intake (3.76 g/kg of BW). Forage canopy characteristics, animal production, and animal behavior on pasture were evaluated in a completely randomized design with three replicates (paddocks). The titanium dioxide marker was used to determine the intake and apparent digestibility during the pasture phase. The production performance in feedlot was evaluated in a completely randomized design with eight replicates (animals). Twenty-four Aberdeen Angus steers (15 ± 1.5 months old; 364.8 ± 21.7 kg BW) were used. At the end of the growth phase the animals were confined and fed a single diet. Dry matter, crude protein, and neutral detergent fiber (NDF) contents of forage and intake were not influenced by the treatments. The average daily gain was higher for the medium intake supplement in pasture phase. The apparent digestibility of NDF (P = and chewing rate were higher for the medium intake supplement. The average daily gain and fat thickness of carcasses were higher for the medium intake supplement in the feedlot phase. The supplements of medium intake increase animal productivity during the growing phase on pastures and cattle’s finishing performance in intensive production systems.

Performance of a concurrent parallel production system through new operating curves of Six Sigma metrics

Performance of a concurrent parallel production system through new operating curves of Six Sigma metrics

Optimal Control Architecture for Balancing Performance and Cost in Oil and Gas Production Systems

Optimal Control Architecture for Balancing Performance and Cost in Oil and Gas Production Systems

Transient analysis of production performance and energy consumption in geometric flexible production systems

To meet the growing need for customized products, flexible production systems are gaining widespread application in modern factories. The inherent complexity of these flexible systems poses new challenges for production management. In this paper, we intend to contribute an operation-level analytical model for such systems, thus providing an effective evaluation tool for productivity and energy consumption analysis. Specifically, a multi-type serial production line with machines obeying the geometric reliability model and intermediate buffers having limited capacity is considered, in which K batches of parts are processed sequentially in each shift. Analytical solutions for key operation metrics are first developed for small systems using a Markov model. For multi-machine lines, a semi-analytical method based on aggregation is proposed. The effectiveness of the proposed analysis method is verified through simulation experiments. Besides, system properties are analyzed to provide insights for continuous improvement and optimization.

A cross-country comparison of pig production systems performance: Evidence from EU countries

This study estimates and analyze the technical efficiency of pork farms from five EU countries. The Generalized True Random-effects (GTRE) model was used to differentiate between persistent and transitory technical efficiency. The results show that elasticities are robust to various specifications. Spanish farms show the highest average efficiency score. In Denmark, Germany, France, and Poland, the most significant opportunities for growth are found in transitional efficiency. The degree of productive specialization in the five countries has a positive impact in the efficiency. In Denmark, Germany, and France, persistent efficiency has a positive association with the fraction of paid labor.

Data-driven decision-making in maintenance management and coordination throughout the asset life cycle: an empirical study

PurposeWith production systems become more digitized, data-driven maintenance decisions can improve the performance of production systems. While manufacturers are introducing predictive maintenance and maintenance reporting to increase maintenance operation efficiency, operational data may also be used to improve maintenance management. Research on the value of data-driven decision support to foster increased internal integration of maintenance with related functions is less explored. This paper explores the potential for further development of solutions for cross-functional responsibilities that maintenance shares with production and logistics through data-driven approaches.Design/methodology/approachFifteen maintenance experts were interviewed in semi-structured interviews. The interview questions were derived based on topics identified through a structured literature analysis of 126 papers.FindingsThe main findings show that data-driven decision-making can support maintenance, asset, production and material planning to coordinate and collaborate on cross-functional responsibilities. While solutions for maintenance planning and scheduling have been explored for various operational conditions, collaborative solutions for maintenance, production and logistics offer the potential for further development. Enablers for data-driven collaboration are the internal synchronization and central definition of goals, harmonization of information systems and information visualization for decision-making.Originality/valueThis paper outlines future research directions for data-driven decision-making in maintenance management as well as the practical requirements for implementation.

Throughput bottleneck detection in manufacturing: a systematic review of the literature on methods and operationalization modes

ABSTRACT Throughput is an important parameter to evaluate production system performance. It is typically constrained by one or more resources referred to as ‘throughput bottlenecks’. To start improvement actions, the first step is to identify throughput bottlenecks. Consequently, several bottleneck detection methods were developed in the literature. But this literature remains largely unstructured, which makes it difficult for practitioners to select an appropriate method. To generate clarity and to consolidate the field, a systematic literature review was conducted. The review identified 14 different bottleneck detection methods that are classified according to the information used: queue states, process states, or combined queue and process states. It further identified three different modes used to operationalize the different bottleneck detection methods: gemba walk, discrete event simulation, and data science. This study further presents important research issues, identifies contingency factors for method application, and discusses important guidelines for the choice of operationalization mode in practice.

Effects of stochastic and heterogeneous worker learning on the performance of a two-workstation production system

Production systems in industries are undergoing transformative changes, with the rise of Industry 4.0 technologies amplifying the complexity of manual and semi-automated workstations, necessitating advanced training and adaptability from human workers. Human workers, due to their unique blend of cognitive and motor skills, thus flexibility, are indispensable and will continue to play a pivotal role. Because of their unique experiences and attributes, they inherently exhibit variability in their processing times and learning rates, which complicates frequent production ramp-ups. Recognizing the lack of comprehensive models that simultaneously account for stochastic processing times and heterogeneous learning during production ramp-ups, this study aims to bridge this gap. We developed an analytical model of a two-worker production system with an intermediate buffer by focusing on worker learning curves, stochastic processing times, and learning heterogeneity. Through an illustrative case, we derived insights into the performance of such systems, specifically in terms of measures including the mean throughput time of a batch, mean waiting time of a part in the buffer, mean idle time of workers, work-in-progress distribution, and buffer usage during the production run. We found that deterministic learning models can significantly underestimate the throughput times, and even consistent average learning rates can lead to variable throughput times based on the learning patterns. Our findings emphasize the need for production managers to consider these factors for realistic and effective production planning, underscoring the novelty of our approach in addressing these intricate dynamics to improve not only system performance, but also worker well-being.

Study on performance of a full spectrum solar-driven hydrogen production system using Ag@SiO2-Au blended nanofluid filters

Study on performance of a full spectrum solar-driven hydrogen production system using Ag@SiO2-Au blended nanofluid filters

Estimation of water consumption and productivity for wheat using remote sensing and SEBAL model: A case study from central clay plain Ecosystem in Sudan

Abstract Remote sensing (RS) can efficiently support the quantification of crop water requirements and water productivity (WP) for evaluating the performance of agricultural production systems and provides relevant feedback for management. This research aimed to estimate winter wheat water consumption and WP in the central clay plain of Sudan by integrating remotely sensed images, climate data, and biophysical modelling. The wheat crop was cultivated under a centre-pivot irrigation system during the winter season of 2014/2015. The Landsat-8 satellite data were used to retrieve the required spectral data. The Surface Energy Balance Algorithm for Land (SEBAL) was supported with RS and climate data for estimating the Actual Evapotranspiration (ETa) and the WP for the wheat crop. The SEBAL outputs were validated using the FAO Penman–Monteith method coupled with field measurements and observation. The results showed that the seasonal ETa ranged from 400 to 600 mm. However, the WP was between 1.2 and 1.5 kg/m3 during the wheat cycle. The spatial ETa and WP maps produced by the SEBAL model and Landsat-8 images can improve water use efficiency at field scale environment and estimate the water balance over large agricultural areas.

Life-cycle assessment of microwave-assisted pyrolysis of pine sawdust as an emerging technology for biodiesel production

Although biofuels are often presented as a renewable and eco-friendly substitute to fossil fuels, it is crucial to have a comprehensive understanding of the complete environmental consequences of biofuel production to make well-informed choices about their usage. This study evaluated the environmental impacts of producing biodiesel from microwave-assisted pyrolysis (MAP) of pine sawdust using life-cycle assessment. A cradle-to-gate system boundary approach encompassing pine cultivation, biomass pre-processing, pyrolysis, and biofuel synthesis via transesterification was used. The ecoinvent v3.7 in openLCA v2.0 software was used to assess the environmental sustainability of the system. The ReCiPe 2016 (H) midpoint impact assessment methodology was used to analyze 18 environmental impact categories. The results showed that producing 1 kg of biofuel from MAP of pine sawdust had environmentally favorable impacts for most categories except global warming potential (1.18 kg CO2 eq.), photochemical oxidant formation (0.71 kg NMVOC eq.), and human toxicity (2.46 kg 1,4-DCB eq.). Biomass production contributed significantly to global warming, freshwater ecotoxicity, human toxicity, and marine ecotoxicity. Pyrolysis contributed 33 % of global warming potential due to non-condensable gas emissions. Biofuel synthesis impacted human toxicity, photochemical oxidant formation, and terrestrial ecotoxicity, mainly due to methanol use. Implementing sustainable practices such as using organic fertilizers, optimization of transportation routes, implementing gas cleaning technologies and effective waste management practices can enhance the environmental performance of this biofuel production system.

Neutrosophic Multi-Criteria Decision-Making Framework for Sustainable Evaluation of Power Production Systems in Renewable Energy Sources

To make the change to a cleaner, more sustainable energy future, it is essential that power production systems be evaluated sustainably. To estimate the overall sustainability performance of various power production systems, this evaluation takes into account a wide range of parameters. Impact on the environment, availability of renewable resources, resource efficiency, social and economic implications, economic feasibility, grid integration and dependability, technical maturity, and scalability are all taken into account. Stakeholders may make better judgments and give higher priority to power production systems that take into account environmental impacts, resource efficiency, social impacts, and economic viability by evaluating these factors. So, we used the multi-criteria decision-making (MCDM) approach VIKOR to assess the power production systems in the sustainability criteria. The VIKOR method is used to rank the several alternatives. We integrated the neutrosophic set with the VIKOR method to deal with inconsistent information. We used seven criteria and ten alternatives in this study. The development of a sustainable and resilient energy sector is aided by the sustainable evaluation of power production systems, which in turn helps to reduce the effects of climate change and safeguard the environment.

Evaluating environmental, economic, and social aspects of an intensive pig production farm in the south of Brazil: a case study

PurposeThe objective of this work was to quantify and understand the impacts of intensive pig production at family-farm level. A case study from the west of Santa Catarina State was used to identify adverse issues (hotspots) of pig production by integrating the assessment of the environmental, economic, and social aspects of the system. The quantitative and qualitative indicators calculated can guide and support the decision-making processes for a variety of stakeholders and actors.MethodsThe environmental performance of the pig production system was assessed from cradle-to-farm gate using environmental Life Cycle Assessment methodology set out in ISO 14040 (ISO 2006a). The functional unit (FU) was 1 kg of Liveweight (kg-LW). The structure of the Life Cycle Sustainability Assessment (LCSA) was based on Neugebauer et al. (J Clean Prod 102:165–176, 2015) and Chen and Holden (J Clean Prod 172:1169–1179, 2018), who proposed a tiered framework to evaluate the impacts on the environmental, social, and financial aspects of a product. The economic dimension or Life Cycle Cost (Hunkeler et al. in Environmental life cycle costing. Crc Press, London, 2008) focused on farm-level activities. The social impact was calculated based on the UNEP/SETAC (2009) guidelines.Results and discussionThe environmental performance of the finishing pig production was slightly lower than reference value for climate impacts, acidification, and eutrophication. The economic impacts tended to be positive, reflecting the efforts of the farmer and employee to maintain high productivity and reduce the number of pig losses in comparison with the reference values. However, this effort did not result in greater profitability, causing low farm income. The impacts of low profitability were not transferred to the employee since the wage were above the reference value. There is a need for more education for small farmers, which is known to have a positive correlation with the adoption of new technologies, thus reducing adverse environmental and social impacts and increasing economic return.ConclusionsThe interaction of social and economic factors suggests it is unlikely that the farm can achieve better environmental performance. The limited economic return and low level of education have a negative impact on the farmer’s capacity to adopt new technologies to improve environmental outcomes. The use of LCSA, based on a consistent model across the three aspects of sustainability, made it possible to understand the interaction of these factors.

The Successful Prediction Of Waterflooding Using A Feed Forward Algorithm

Waterflooding is one of the most frequently used EOR methods to increase oil recovery because it can increase 30% -60% of total production. It is necessary to apply a production system performance prediction approach to minimize uncertainty in increasing production figures, such as analytical methods and numerical methods. Artificial Intelligence in the world of oil and gas is not a new thing, but it has often been used in various fields such as exploration, drilling, production, and reservoirs. So this is the basis for the prediction of the success of waterflooding research carried out. The purpose of this research was to predict the success rate of waterflooding using an Artificial Neural Network (ANN). The method used in this study is the simulation research method using CMG Imex for reservoir simulation modeling, running CMG CMOST for 500 sensitivity data with the input of seven parameters of compressibility, horizontal permeability, vertical permeability, pressure injection, injection rate, thickness, oil saturation, and the output is recovery factor using Artificial Neural Network (ANN) with a ratio of 70% of the RF calculation model results for training and 30% model results for testing. In order to get optimal prediction results, trial, and error were carried out on the number of hidden layer nodes, so that optimal and stable hidden layer nodes were obtained at node 10 with RMSE values of 0.339035 for training and 0.442663 for testing and MAPE for training 1.15% and 1.62% for testing. The statistical analysis value is 0.906139 for training and 0.899525 for testing data. It can be concluded from this study that the use of ANN in predictions using ten hidden layer nodes proved to be very good and successful, and predictions in this study were classified as High Accurate Prediction.

Tilapia aquaculture systems in Egypt: Characteristics, sustainability outcomes and entry points for sustainable aquatic food systems

The future demand for fish and other aquatic foods requires the sustainable intensification of related production systems. However, policy and investment decisions for the sustainable intensification of aquaculture systems are usually hindered by the lack of benchmarking data about their actual sustainability performance, often resulting in poorly developed and implemented interventions that ignore potential sustainability trade-offs. This is a reality in many of the leading aquaculture producers in the developing world like Egypt. In this study we analyzed farm-level data from 402 aquaculture producers in the Kafr El Sheikh governorate in Egypt, to characterize and benchmark the performance of tilapia production systems against key sustainability outcomes. For the analysis we used a combination of statistical tools such as ordinary least square regressions, simultaneous quantile regressions and propensity score matching. We focussed on how the production characteristics and practices of different tilapia production systems intersect with economic, food security, and environmental outcomes that cover multiple dimensions of sustainability. We found that differences in these production characteristics and practices were significantly associated with the sustainability performance of tilapia production systems. In particular, our results show that yields in monocultural systems (10,460.5 ton/ha) were significantly higher than in polyculture systems (8404.7 ton/ha). Furthermore, despite the generally positive economic, food security, and environmental outcomes of several of the studied systems, some trade-offs emerge both between and within these sustainability dimensions.

An Industrial Case Study on the Monitoring and Maintenance Service System for a Robot-Driven Polishing Service System under Industry 4.0 Contexts

Remote monitoring and maintenance are important for improving the performance of production systems. However, existing studies on this topic usually focus on the monitoring and maintenance of the working conditions of the equipment and pay relatively less attention to the processing craft and processing quality. In addition, as far as we know, there are relatively few industrial case studies on the real applications of remote monitoring and maintenance systems that include both conventional and advanced maintenance techniques under the context of Industry 4.0. Addressing these issues, an industrial case study on the monitoring and maintenance service system for a robot-driven carbon block polishing service system is presented, including its application background and engineering problems, software/hardware architecture and running logic, the monitoring and maintenance-related enabling techniques, and the configuration and operation workflows of the system in the form of screenshots of the functional WebAPPs of the software system. The case study can provide real examples and references for the industrial application of remote monitoring and maintenance service systems on industrial product service systems under the context of Industry 4.0. Advanced techniques such as the Industrial Internet of Things, digital twins, deep learning, and edge/cloud/fog computing have been applied to the system.

Performance Analysis and Comparison of an Experimental Hybrid PV, PVT and Solar Thermal System Installed in a Preschool in Bucharest, Romania

The demand for on-site production of energy is showing a rapid increase as the trend of decentralisation and energy self-reliance gains momentum. This paper studies and compares three of the main solar energy technologies: photovoltaic, solar thermal panels and hybrid photovoltaic thermal panels. A prototype experimental installation consisting of the aforementioned technologies was set up on the campus of University Politehnica Bucharest. Data were collected over several months, then the instantaneous power production and overall system performance was computed. The system was analysed in four types of weather patterns, and its suitability was assessed in each case. The results show that the performance of PVT panels is closely connected to the dissipation of the thermal energy collected in the thermal storage tank. In addition, PVT collectors can outperform the PV panels in accordance to the thermal energy demand of the end user when used in an installation with suitable dimensions.