Expected Production Rate Research Articles

Solar, Atmospheric, and Volcanic Impacts on 10Be Depositions in Greenland and Antarctica During the Last 100 Years

AbstractCosmogenic radionuclides (e.g., 10Be) from ice cores are a powerful tool for solar reconstructions back in time. However, superimposed on the solar signal, other factors like weather/climate and volcanic influences on 10Be can complicate the interpretation of 10Be data. A comprehensive study of 10Be records over the recent period, when atmospheric 10Be production and meteorological conditions are relatively well‐known, can improve our interpretation of 10Be records. Here we conduct a systematic study of the production and climate/volcanic signals in Antarctica and Greenland 10Be records, including a new 10Be record from the East GReenland Ice‐core Project site. Greenland and Antarctica records show significant decreasing trends (5%–6.5%/decade) for 1900–1950, which is comparable with the expected production rate inferred from sunspot observations. By comparing 10Be records with reanalysis data and atmospheric circulation patterns, 10Be records from Southern/Southeastern Greenland are significantly correlated with the Scandinavia pattern. Stacking 10Be records from different locations can enhance the production signal. However, this approach is not always straightforward as uncertainties in some records can lead to a weaker solar signal. A strategy can be employed to select records for the bipolar stack by comparing Greenland records with Antarctica records, assuming the shared signal is a production signal. Finally, we observe significant increases (36%–64%) in 10Be depositions in Greenland related to the Agung eruption. This large increase in Greenland 10Be records after the Agung eruption, could be partly explained by the enhanced air mass transport from mid‐latitudes coinciding with the decreased precipitation en‐route.

Analysis of Line Balancing Problem for Production of Fuel Tank Mounting Bracket

Assembly line balancing is a manufacturing technique that establishes an expected production rate to create a specific product in a particular period of time. Six Sigma is a profit-maximizing technique achieved by meeting consumer satisfaction. Cycle time reduction has emerged as a crucial aspect of improvement to generate high production and fulfill client demands. In industrial production, assembly lines play a vital role as flow-line production systems. Assembly lines need a significant capital expenditure to build or redesign them, but with good configuration, cost-effective manufacturing is possible. This study aims to increase assembly line productivity with shorter cycle times. The main stage in enhancing an assembly line's overall performance is to create or reconfigure an assembly system, which is made possible by assembly line balance and related operations analysis.

E&P Notes (April 2022)

E&P Notes (April 2022)

Modelling the startup of machine tools for energy efficient multi-sleep control policies

Energy efficient control strategies have been of recent interest in literature as promising measures to reduce the energy consumed by production equipment during interruptions of part flow. A general time-based control policy is analyzed. Machine idle times are assumed stochastic and the expected value of the energy consumed per produced part is reduced while assuring a certain target of expected production rate. The startup time required to resume the service depends on the time period the machine stays in a low power consumption state. Since the startup is often related to one or more machine components, the optimal policy might be to partially switch the machine. Therefore, a policy acting at component level is also analyzed where the duration of the startup depends on the set of machine components that are switched off. The policy is compared to state-of-the-art policies and discussed for a set of simulated numerical cases representing several production environments.

Modularity and Convertibility Characteristics of Reconfigurable Manufacturing System : An Approach for Operations Management

Modularity and Convertibility Characteristics of Reconfigurable Manufacturing System : An Approach for Operations Management

Production rate measurement of Tritium and other cosmogenic isotopes in Germanium with CDMSlite

Future direct searches for low-mass dark matter particles with germanium detectors, such as SuperCDMS SNOLAB, are expected to be limited by backgrounds from radioactive isotopes activated by cosmogenic radiation inside the germanium. There are limited experimental data available to constrain production rates and a large spread of theoretical predictions. We examine the calculation of expected production rates, and analyze data from the second run of the CDMS low ionization threshold experiment (CDMSlite) to estimate the rates for several isotopes. We model the measured CDMSlite spectrum and fit for contributions from tritium and other isotopes. Using the knowledge of the detector history, these results are converted to cosmogenic production rates at sea level. The production rates in atoms/(kg · day) are 74 ± 9 for 3H, 1.5 ± 0.7 for 55Fe, 17 ± 5 for 65Zn, and 30 ± 18 for 68Ge.

Application of lean manufacturing principles to improve a conceptual 238Pu supply process

The mission of the United States (U.S.) Department of Energy’s Pu-238 Supply Project is to rebuild capability to produce 238Pu at the kilogram scale in the U.S. This radioisotope is used by the National Aeronautics and Space Administration (NASA) to power deep space probes, and the supply is dwindling. It was last produced in the U.S. in 1988. A conceptual design of a 238Pu supply process is described that uses existing processes and facilities at Oak Ridge National Laboratory’s Radiochemical Engineering Development Center. The rate-limiting section of the conceptual process was analyzed using discrete-event system simulation to determine expected production rates, bottlenecks, and the effects of time delays on the production rate. Process alternatives were generated based on Lean Manufacturing principles, and those were examined and compared to the original process using simulation to identify better operating strategies.

Numerical simulation on gas production from hydrate reservoir at the 1st offshore test site in the eastern Nankai Trough

According to the available geological data of hydrate reservoir at the 1st offshore production site in the Eastern Nankai Trough in 2013, a simulation model matching the production test scheme was established to investigate a relatively long-term production performance and the effect of perforated interval on gas production by comparing the short simulation results and the field test data (6 days). The verification results indicate that the simulated gas production rate is consistent with the measured field value but a certain deviation of corresponding water production rates occurs. The relatively long-term simulation performed reveal that both gas released rate in the sediment and gas production rate in the well increase before complete dissociation of hydrates between bottom hole and underburden. After that, they convert to decreasing and the water production rate increases sharply. Meanwhile, the slight secondary hydrates are observed in the dissociation front. Additionally, the sensitivity analysis of completion interval indicates that the perforated interval design for candidate hydrate reservoir like this site should avoid the low-permeability hydrate layer and take the lower hydrate layers as the water blocking zones when using depressurization method, and that the optimum completion interval should be determined by together considering the production period and the expected production rate.

New approach for robust multi-objective optimization of turning parameters using probabilistic genetic algorithm

In this paper, a contribution to the determination of reliable cutting parameters is presented, which is minimizing the expected machining cost and maximizing the expected production rate, with taking into account the uncertainties of uncontrollable factors. The concept of failure probability of stochastic production limitations is integrated into constrained and unconstrained formulations of multi-objective optimization problems. New probabilistic version of the nondominated sorting genetic algorithm P-NSGA-II, which incorporates the Monte Carlo simulations for accurate assessment of cumulative distribution functions, was developed and applied in two numerical examples based on similar and anterior work. In the first case, it is a question of the search space that is completely ‘closed’ by high natural variability related to the multi-pass roughing operation: in this case, the failure risk of technological limitations are considered as objectives to minimize with economic objectives. The second case is related to deformed search space due to the uncertainties specific to finishing operation; therefore, the economic objectives are minimized under imposed maximum probabilities of failure. In both situations, the efficiency and robustness of optimal solutions generated by the P-NSGA-II algorithm are analysed, discussed and compared with sequence of unconstrained minimization technique (SUMT) method.

Two efficient heuristics to solve the integrated load distribution and production planning problem

This paper considers a multi-period production system where a set of machines are arranged in parallel. The machines are unreliable and the failure rate of machine depends on the load assigned to the machine. The expected production rate of the system is considered to be a non-monotonic function of its load. Because of the machine failure rate, the total production output depends on the combination of loads assigned to different machines. We consider the integration of load distribution decisions with production planning decision. The product demands are considered to be known in advance. The objective is to minimize the sum of holding costs, backorder costs, production costs, setup costs, capacity change costs and unused capacity costs while satisfying the demand over specified time horizon. The constraint is not to exceed available repair resources required to repair the machine breakdown. The paper develops two heuristics to solve the integrated load distribution and production planning problem. The first heuristic consists of a three-phase approach, while the second one is based on tabu search metaheuristic. The efficiency of the proposed heuristics is tested through the randomly generated problem instances.

Robust Production Capacity Planning of a Wafer Fabrication System with Uncertain Wafer Lots Transfer Probabilities

Production capacity planning is an important problem in the design of a semiconductor wafer fabrication system, which means to optimize the number of tools in the system so that expected production rate, work-in-process level can be satisfied and the cost of building the wafer fab can be minimized. However, a wafer fab usually produces multiple types of wafers, different types of wafers have different and complicated processing routes, and the work-in-process quantities of different types of wafers changes frequently due to the fluctuation of demands. Therefore, the transfer probabilities of wafer lots among tools, material handle devices and bays in the wafer fab are uncertain. To deal with this uncertainty, it is necessary to improve the robustness of he production capacity planning of a wafer fab. In this paper, we construct an open queueing network-based model of a wafer fab and developed a mathematical model of the robust production capacity planning problem with the objective of maximizing the probability of satisfying expected performance requirements. To solve this problem, a heuristic algorithm is proposed and numerical experiments are conducted to evaluate the robustness of the solution.

Issues and solutions for intensive plantation silviculture in a context of ecosystem management

Forest plantations are recognized as a silvicultural tool for ensuring a timber supply that meets public expectations regarding sustainable forest management. However, they are also part of the silvicultural scenario that shows the greatest potential for the artificialization of natural forests. From a firsthand perspective, intensive plantation silviculture objectives may appear antagonistic to those of ecosystem management. Here we describe the process through which we defined and documented plantation issues, then propose potential solutions to allow the integration of intensive plantation silviculture into ecosystem management. We identify issues related to the scale, localization and spatial arrangement of plantations, the key attributes and resilience of natural forests, social acceptability, and the productivity and profitability of plantations. We also propose potential solutions likely to help manage plantations within a context of ecosystem management. These include modulating silvicultural treatments to enhance the naturalness of plantations, conducting treatments to obtain expected production rates, and ensuring that plantations are deployed across the landscape in a manner that integrates stakeholder concerns and considers the naturalness of the forest matrix.

Moments of accumulated reward and completion time in Markovian models with application to unreliable manufacturing systems

Performance evaluation models are used by companies to design, adapt, manage and control their production systems. In the literature, most of the effort has been dedicated to the development of efficient methodologies to estimate the first moment performance measures of production systems, such as the expected production rate, the buffer levels and the mean completion time. However, there is industrial evidence that the higher moments of the production output may drastically impact on the capability of managing the system operations, causing the observed system performance to be highly different from what expected. This paper presents a methodology to analyze the cumulated output and the lot completion time moments of Markovian reward models. Both the discrete and continuous time cases are considered. The technique is applied to unreliable manufacturing systems characterized by general Markovian structures. Numerical results show how the theory developed in this paper can be applied to analyse the dependency of the output variability and the service level on the system parameters. Moreover, they highlight previously uninvestigated features of the system behavior that are useful while operating the system in practical settings.

Effects of the location-based management system on production rates and productivity

Location-based management systems (LBMSs) are becoming more commonly used to plan and control production in construction projects. The main use has been to compress durations and improve resource efficiency through real-time production control and forecasting. LBMS theory proposes that instead of continuously updating the plan with actualized completion dates, as with the critical path method (CPM), control actions can be used to manage an effective response to deviations in production in order to realign the schedule forecast with the approved plan. Three healthcare construction projects in California were monitored to document the effects of planning and control decisions on production rates, resources and labour consumption. First, the authors hypothesize that proposed control actions based on the system led to real actions in the field. Second, these control actions helped in controlling production rates. Third, control actions decreased subcontractor labour consumption. The results show that control actions were implemented as a response to production alarms and half of the implemented control actions were able to prevent production problems. Many control actions successfully improved production and labour consumption rates with a long-term impact. However, adding resources often increased labour consumption and negated part of the expected production rate benefit.

Analytical evaluation of the output variability in production systems with general Markovian structure

Performance evaluation models are used by companies to design, adapt, manage and control their production systems. In the literature, most of the effort has been dedicated to the development of efficient methodologies to estimate the first moment performance measures of production systems, such as the expected production rate, the buffer levels and the mean completion time. However, there is industrial evidence that the variability of the production output may drastically impact on the capability of managing the system operations, causing the observed system performance to be highly different from what expected. This paper presents a general methodology to analyze the variability of the output of unreliable single machines and small-scale multi-stage production systems modeled as General Markovian structure. The generality of the approach allows modeling and studying performance measures such as the variance of the cumulated output and the variance of the inter-departure time under many system configurations within a unique framework. The proposed method is based on the characterization of the autocorrelation structure of the system output. The impact of different system parameters on the output variability is investigated and characterized. Moreover, managerial actions that allow reducing the output variability are identified. The computational complexity of the method is studied on an extensive set of computer experiments. Finally, the limits of this approach while studying long multi-stage production lines are highlighted.

A Web-Based Decision Support System for Analyzing Timber Harvesting Costs and Productivity

An interactive web-based decision support system (DSS) was developed to assist the analysis of harvesting costs and productivity of roundwood operations in the Appalachian region. The DSS was designed using the ASP.NET platform via the VB.NET language and Microsoft (MS) SQL server. Three web pages were created: a main page for system configuration and simulation, a machine rate page for computing the handling machine costs requested by the main page, and a summary page for displaying all results. Stands, machines, and harvesting systems were modeled using previously published time and motion studies. An MS SQL server was used to retrieve the default information, to save the running results, and to populate the summary tables. A mathematical algorithm was designed to balance the harvesting system based on the machine productivity and use rates. The DSS was used to run three central Appalachian hardwood region‐based simulations. The first simulation system was a chainsaw/cable skidder, the second was a feller-buncher/grapple skidder, and the final harvesting system used a harvester/forwarder. The simulation results were summarized including hourly machine rates and costs, system rate/cost, and the expected production rate for each harvesting system. The innovative DSS provides useful harvesting management decisionmaking information for loggers.

Optimal control of a stochastic production-inventory model with deteriorating items

This paper considers a stochastic optimal control of an inventory model with a deterministic rate of deteriorating items. The dynamics of the inventory model includes a perturbation by a Wiener process. The paper uses Hamilton–Jacobi–Bellman principle to find a nonlinear partial differential equation that the value function must satisfy. The partial differential equation is solved by assuming a particular form for the solution and finding three functions Q(t), M(t), and R(t) of time by substituting the assumed solution form back in the partial differential equation. The paper then proceeds to find the optimal expected production rate and the optimal expected inventory level. The paper discusses some special cases for specific parameter values and provides some numerical examples.

A Study of the Condition of Timber Production in Iran and the Expected Production Rate in the Next Decade

In countries with industrial forests, the first resource for providing domestic timber is their productive forests. Iran, a country that holds a few timber forests in the north, has implemented deterministic programming in the past few decades. Knowing the process of timber production in these forests and also the condition of these products that are affected by the above mentioned programming is very crucial. Therefore, the present study focuses on the timber production of Iran’s northern forests in a 33-year period (from 1978 to 2010), using simple regression analysis and time series. The results of square regression analysis revealed that this process has a significant relationship with the years under study and shows inconsistent changes. Furthermore, the overall inflation in domestic market of Iran has the highest effect on the deterministic programming of its northern forests. To predict timber production in Iran Auto-regressive moving average process 1-2 was employed, verifying the fact that the production rate will decline to about 740 thousand cubic meters. If current trends and decline in timber production continue, Iran would become increasingly dependent on imported timber. To avoid such dependence, or at least reduce it, it is necessary to change the present programming system from deterministic to dynamic, taking into account the internal and external risks in timber market such as inflation and average timber price.

PERSPECTIVES OF THE NON-MESONIC WEAK DECAY IN DOUBLE HYPERNUCLEI

The features of the Hyperon Induced Non Mesonic Weak Decay in double hypernuclei are shortly reviewed and a preliminary evaluation of the expected production rate in the future PANDA experiment at FAIR is reported.

Moments of Cumulated Output and Completion Time of Unreliable General Markovian Machines

AbstractPerformance evaluation models are used by companies to design, adapt, manage and control their production systems. In the literature, most of the effort has been dedicated to the development of efficient methodologies to estimate the first moment performance measures of production systems, such as the expected production rate, the buffer levels and the mean completion time. However, there is industrial evidence that the variability of the production output may drastically impact on the capability of managing the system operations, causing the observed system performance to be highly different from what expected. This paper presents a general theory and a methodology to analyze the cumulated output and the lot completion time variability of unreliable machines and systems characterized by general Markovian models. Both discrete models and continuous reward models are considered. We then discuss two simple examples that show how the theory developed in this paper can be applied to analyse the dependency of the output variability on the system parameters.