Evaluating Variability of Automatic Process Control of the Moisture Control in Medium Density Fibreboard Line, Using Statistical Process Control

Hernández, M. Suárez; Novoa, Francisco

doi:10.1109/tla.2020.9082910

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…e APC strategy is widely used in product, manufacture, and service fields. Hernández et al [10] put forward a control tool to monitor variables. Yuan et al [11] studied an APC chart to identify exceptions.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Consumption Load Monitoring and Adjustment Strategy in Smart Grid

Xiao

Dai

2021

Journal of Mathematics

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The enhancement of the intelligent construction of the power grid and widespread popularity of smart meters enable large amounts of electrical energy consumption data to be collected and analyzed. Based on the data, the energy provider gives a guiding price in the future periods to users. It encourages users to be more economical and smarter in the process of using electricity. By applying the social welfare model to equate demand and supply in every time interval, we gain the optimal prices and generation capacity. Nevertheless, the truth is that there is a great gap between the consumers’ booked electrical energy consumption and the optimal generation capacity, causing the power system overload and even outage. This article puts forward a novel automatic process control strategy in order to monitor the gap between the consumers’ booked electrical energy consumption and optimal generation capacity by using statistical method to predict the future one. When the predicted value exceeds the boundary, the energy provider adopts the changeable electricity price to stimulate consumers to adjust their electrical energy demands so that it can have smoothly actual electrical energy consumption. Our adjustment method is data-driven exponential function-based adjustment. Case study results show that the strategy can obtain small adjustment times, stable actual consumption load, and controllable prediction errors. Different from the linear monitoring and adjustment strategy, our approach obtains almost the same adjustment frequency, less standard deviation of residuals, and higher total social welfare and energy provider profit.

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Section: Introductionmentioning

confidence: 99%

Data-Driven Consumption Load Monitoring and Adjustment Strategy in Smart Grid

Xiao

Dai

2021

Journal of Mathematics

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Fuzzy tree classification system for fault diagnosis on Ion implanter

Horng

Lin

2021

J. Phys.: Conf. Ser.

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Ion implanter is critical to modern integrated-circuit (IC) manufacturing. Ion implanter requires a long process due to low acceleration and high concentration process, which results in low productivity and becomes a bottleneck in the semiconductor fabrication. Furthermore, the wafer is not re-workable if existing error operation either caused by the operator or the machine. Therefore, it is important to develop a real-time fault detection system to minimize the probable down time of the ion implanter. In this work, a real-time fault detection system, which is based on the Fuzzy Tree Classification Systems (FTCS), is proposed to monitor the operation of the Eaton NV6200A/AV ion implanter. Two datasets, the 26-recipe and the 42- recipe cases, provided by a renowned wafer foundry in Taiwan were used to test the performance of the proposed FTCS. The datasets were obtained through the SECS-II interface. Test results demonstrate that the proposed FTCS can work real-time for fault detection of the Eaton NV6200A/AV ion implanter.

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