Development of priority oriented scheduling method to increase the efficiency and reliability for automotive job

Scheduling occurs in every organization without considering the nature of its activities. In this regard, numerous scholars have attempted to schedule via divergent methods including classical scheduling, genetic algorithm, neural network, fuzzy logic, and so on. Studies in manufacturing scheduling mostly deal with priority rules without considering the system states. An appropriate scheduling leads to significant enhancement of fairness in job scheduling. The term fairness can be transformed into a specific selection of job weights. There is no method of scheduling in which “Priority, Time Action (duration), and Time Stamp” of jobs have simultaneously been considered. The proposed method of scheduling can enhance the efficiency and reliability of manufacturing systems via considering aforementioned aspects. To fulfill this target, first and foremost, the normalize method should be performed. This method allows data (time stamp, time action,priority) of jobs on different scales to be compared by bringing those to a common scale. Secondly, the jobs should be arranged based on three criteria which are priority, time action and time stamp. This sorting algorithm is programmed via MATLAB distributed computing server (DCS) software. Eventually, to evaluate the proposed method of scheduling, simulation is operated. The simulated algorithm shows that applying the proposed method of scheduling increases the efficiency of simulated scheduler in comparison with common method of scheduling. Besides the mentioned simulated algorithm, there is a mathematical proof to prove the enhancement of reliability. Also, to evaluate the proposed method of scheduling, a case study in an automotive manufacturing company (IKCO) is conducted. In this case study, cause and effect analysis was employed. Therefore, the causes derived from ignoring priority and time action are determined. By applying the proposed method of scheduling, the mentioned causes are spontaneously eliminated. To show the significant difference between efficiency of system before and after applying proposed method of scheduling pair sample t-test is employed. Also, this test is operated to show the significant difference between reliability of system after and before employing the proposed method of scheduling. Finally, the provided results (Sig. (2-tailed) = 0) show that there are remarkable enhancement in efficiency and reliability of system by applying the proposed method of scheduling

Data Analyses of Quarry Operations and Maintenance Schedules: A Production Optimization Study

In this research, data analytics and machine learning were used to identify the performance metrics of loaders and haul trucks during mining operations. We used real-time collected data from loaders and haul trucks operating in multiple quarries to broaden the scope of the study and remove bias. Our model indicates relationships between multiple variables and their impacts on production in an operation. Data analysis was also applied to ground engagement tools (GET) to identify key preventative maintenance schedules to minimize production impact from capital equipment downtime. Through analysis of the loader’s data, it was found there is an efficient cycle time of around 35 s to 40 s, which yielded a higher payload. The decision tree classifier algorithm created a model that was 87.99% accurate in estimating the performance of a loader based on a full analysis of the data. Based on the distribution of production variables across each type of loader performing in a similar work environment, the Caterpillar 992K and 990K were the highest-yielding machines. Production efficiency was compared before and after maintenance periods of ground engaging tools on loader buckets. With the use of maintenance and production records for these tools, it was concluded that there was no distinguishable change in average production and percentage change in production value before and after maintenance days

A Review of Gas Injection in Shale Reservoirs: Enhanced Oil/Gas Recovery Approaches and Greenhouse Gas Control

Shale oil and gas resources contribute significantly to the energy production in the U.S. Greenhouse gas emissions come from combustion of fossil fuels from potential sources of power plants, oil refineries, and flaring or venting of produced gas (primarily methane) in oilfields. Economic utilization of greenhouse gases in shale reservoirs not only increases oil or gas recovery, but also contributes to CO2 sequestration. In this paper, the feasibility and efficiency of gas injection approaches, including huff-n-puff injection and gas flooding in shale oil/gas/condensate reservoirs are discussed based on the results of in-situ pilots, and experimental and simulation studies. In each section, one type of shale reservoir is discussed, with the following aspects covered: (1) Experimental and simulation results for different gas injection approaches; (2) mechanisms of different gas injection approaches; and (3) field pilots for gas injection enhanced oil recovery (EOR) and enhanced gas recovery (EGR). Based on the experimental and simulation studies, as well as some successful field trials, gas injection is deemed as a potential approach for EOR and EGR in shale reservoirs. The enhanced recovery factor varies for different experiments with different rock/fluid properties or models incorporating different effects and shale complexities. Based on the simulation studies and successful field pilots, CO2 could be successfully captured in shale gas reservoirs through gas injection and huff-n-puff regimes. The status of flaring gas emissions in oilfields and the outlook of economic utilization of greenhouse gases for enhanced oil or gas recovery and CO2 storage were given in the last section. The storage capacity varies in different simulation studies and is associated with well design, gas injection scheme and operation parameters, gas adsorption, molecular diffusion, and the modelling approaches

A Comprehensive Numerical Model for Simulating Fluid Transport in Nanopores

Since a large amount of nanopores exist in tight oil reservoirs, fluid transport in nanopores is complex due to large capillary pressure. Recent studies only focus on the effect of nanopore confinement on single-well performance with simple planar fractures in tight oil reservoirs. Its impacts on multi-well performance with complex fracture geometries have not been reported. In this study, a numerical model was developed to investigate the effect of confined phase behavior on cumulative oil and gas production of four horizontal wells with different fracture geometries. Its pore sizes were divided into five regions based on nanopore size distribution. Then, fluid properties were evaluated under different levels of capillary pressure using Peng-Robinson equation of state. Afterwards, an efficient approach of Embedded Discrete Fracture Model (EDFM) was applied to explicitly model hydraulic and natural fractures in the reservoirs. Finally, three fracture geometries, i.e. non-planar hydraulic fractures, nonplanar hydraulic fractures with one set natural fractures, and non-planar hydraulic fractures with two sets natural fractures, are evaluated. The multi-well performance with confined phase behavior is analyzed with permeabilities of 0.01 md and 0.1 md. This work improves the analysis of capillarity effect on multi-well performance with complex fracture geometries in tight oil reservoirs.National Natural Science Foundation of China [51674010]; National Science and Technology Major Project of China [2016ZX05014]; China Scholarship Council (CSC) [201506010205]SCI(E)ARTICLE

Multi-scale equation of state computations for confined fluids

Fluid properties of five binary mixtures relevant to shale gas and light tight oil in confined nano-channels are studied. Canonical (NVT) Monte Carlo simulations are used to determine internal energies of departure of pure fluids using the RASPA software system (Dubbeldam et al., 2015). The linear mixing rule proposed by Lucia et al. (2012) is used to determine internal energies of departure for mixtures, UMD, in confined spaces and compared to UMD from direct NVT Monte Carlo simulation. The sensitivity of the mixture energy parameter, aM, for the Gibbs-Helmholtz constrained (GHC) equation, confined fluid molar volume, VM, and bubble point pressure are studied as a function of uncertainty in UMD. Results show that the sensitivity of confined fluid molar volume to 5% uncertainty in UMD is less than 1% and that the GHC equation predicts physically meaningful reductions in bubble point pressure for light tight oils