The optimal power flow (OPF) module optimizes the generation, transmission, and distribution of electric power without disrupting network power flow, operating limits, or constraints. Similarly to any power flow analysis technique, OPF also allows the determination of system’s state of operation, that is, the injected power, current, and voltage throughout the electric power system. In this context, there is a large range of OPF problems and different approaches to solve them. Moreover, the nature of OPF is evolving due to renewable energy integration and recent flexibility in power grids. This paper presents an original hybrid imperialist competitive and grey wolf algorithm (HIC-GWA) to solve twelve different study cases of simple and multiobjective OPF problems for modern power systems, including wind and photovoltaic power generators. The performance capabilities and potential of the proposed metaheuristic are presented, illustrating the applicability of the approach, and analyzed on two test systems: the IEEE 30 bus and IEEE 118 bus power systems. Sensitivity analysis has been performed on this approach to prove the robustness of the method. Obtained results are analyzed and compared with recently published OPF solutions. The proposed metaheuristic is more efficient and provides much better optimal solutions.
In today's competitive market place, manufacturing companies must apply continuous process improvement in order to maintain a returning customer base. One way of achieving constant process improvement is through value stream mapping. Value stream mapping is used to visualize the current processes for easier understanding and problem identification. A well-defined problem statement will ensure a successful outcome of a project improvement process. This research provides a case study performed on a rope manufacturing process. A current state value stream map is created, and the possible improvements are suggested. The implemented results are shown in the form of future state map. The results show that, after waste elimination and structural revision, a manufacturing process becomes more efficient, enabling the customer to receive an order significantly faster.
PurposeThe purpose of this case study is to develop a lean six sigma (LSS) define–measure–analyze–improve–control (DMAIC) procedure to optimize the willingness to respond (WTR) of Louisiana-based law enforcement officials (LEO) to disasters.Design/methodology/approachVarious LSS tools were used to implement DMAIC to clearly define the problem of WTR, measure the self-reported WTR through a survey, perform statistical analysis on the measured data to identify significant variables to WTR, brainstorm issues and improvements with stakeholders, develop mitigation strategies, implement a pilot solution survey and develop control measures.FindingsLouisiana LEO showed an average of 73.9% of WTR to all disasters. Seven significant variables influenced WTR, which are prior experience, transportation, duty to organization, risk to self, conflicting roles, training and incentive pay. The results from pilot solutions showed that utilizing incentive pay, adequate training and personal protective equipment (PPE) increased WTR from 0.5% up to 16%.Originality/valueThis study developed and validated a unique procedure to improve LEO WTR to disasters, providing a set of DMAIC tools and concepts that can be used by other emergency response agencies. This research was performed during the COVID-19 pandemic and after Hurricane Laura impacted Louisiana.
Pyrite is a common mineral with a higher density than most other minerals in the Eagle Ford Shale formation. Hence, if pyrite is not considered in the total organic carbon (TOC) estimation, based on density logs, it may lead to errors. In order to improve the accuracy of the TOC estimation, we propose an updated TOC estimation method that incorporates the concentration of pyrite and organic porosity. More than 15 m of Eagle Ford Shale samples were analyzed using Rock-Eval pyrolysis, X-ray fluorescence (XRF), and X-ray diffraction (XRD). TOC, elemental concentration, and mineralogical data were analyzed for a better understanding of the relationship between the concentration of TOC and pyrite content in the Eagle Ford formation. An updated petrophysical model-including parameters such as organic pores, solid organic matter, inorganic pores, pyrite, and inorganic rock matrix without pyrite-was built using the sample data from the Eagle Ford. The model was compared with Schmoker's model and validated with the Eagle Ford field data. The results showed that the updated model had a lower root mean square error (RMSE) than Schmoker's model. Therefore, it could be used in the future estimation of TOC in pyrite-rich formations.
This paper addresses the application of Design for Six Sigma (DFSS) methodology to the design of a marine riser joint hydraulic line test fixture. The original test fixture was evaluated using Value Steam Mapping (VSM) and appropriate Lean design tools such as 3D Modeling and Finite Element Analysis (FEA). A new test fixture was developed which resulted in improving the process cycle efficiency for the test from 25% to 50% percent, leading to a 50% reduction in test cost. Handling of the new test fixture is greatly improved as compared to the original fixture.
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