Demand Forecasting for Real-Time Operational Control

The piston pin punching recess automatic drilling machine tool, is a dedicated machine tool designed and manufactured to solve the possess of piston pin punching recess. This paper studies the design of the dedicated machine’s control system, which uses open CNC technology, hydraulic technology, PLC technology, electrical technology, and human-machine interface technology. The control system can achieve the automation of feeding and blanking process. Replace of control panel, adopting touch screen to achieve operational control, and monitor the status of real-time job on the machine.

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Real Time Control of Wastewater Treatment Operations

Water Science & Technology ◽

10.2166/wst.1992.0484 ◽

1992 ◽

Vol 25 (4-5) ◽

pp. 89-101 ◽

Cited By ~ 8

Author(s):

V. Novotny ◽

A. Capodaglio ◽

H. Jones

Keyword(s):

Wastewater Treatment ◽

Real Time ◽

Operational Control ◽

Real Time Control ◽

Time Control ◽

Treatment Processes ◽

Treatment Facilities ◽

And Performance ◽

Time Invariant ◽

Near Future

Real Time Control (RTC) of wastewater treatment processes is an alternative to the present static, or time-invariant, mode of control during which wastewater in excess of a predetermined capacity is bypassed or diverted into an overflow. RTC control relies on modeling to predict the near future (i.e. next operation interval) influent loads, parameters and performance of the system and effluent water quality characteristics. Based on these predictions the RTC system and/or the operator can adjust and optimize the performance of the system using the control options built into the system itself. The paper describes the concepts of the RTC operational control of wastewater treatment facilities, modeling techology, strategy and applications.

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Data-Driven Real-Time Online Taxi-Hailing Demand Forecasting Based on Machine Learning Method

Applied Sciences ◽

10.3390/app10196681 ◽

2020 ◽

Vol 10 (19) ◽

pp. 6681 ◽

Cited By ~ 1

Author(s):

Zhizhen Liu ◽

Hong Chen ◽

Xiaoke Sun ◽

Hengrui Chen

Keyword(s):

Real Time ◽

Public Transportation ◽

Demand Forecasting ◽

Gradient Boosting ◽

Intelligent Transport System ◽

Transportation Services ◽

Intelligent Transport ◽

Time Prediction ◽

Extreme Gradient Boosting ◽

Demand Forecasting Model

The development of the intelligent transport system has created conditions for solving the supply–demand imbalance of public transportation services. For example, forecasting the demand for online taxi-hailing could help to rebalance the resource of taxis. In this research, we introduced a method to forecast real-time online taxi-hailing demand. First, we analyze the relation between taxi demand and online taxi-hailing demand. Next, we propose six models containing different information based on backpropagation neural network (BPNN) and extreme gradient boosting (XGB) to forecast online taxi-hailing demand. Finally, we present a real-time online taxi-hailing demand forecasting model considering the projected taxi demand (“PTX”). The results indicate that including more information leads to better prediction performance, and the results show that including the information of projected taxi demand leads to a reduction of MAPE from 0.190 to 0.183 and an RMSE reduction from 23.921 to 21.050, and it increases R2 from 0.845 to 0.853. The analysis indicates the demand regularity of online taxi-hailing and taxi, and the experiment realizes real-time prediction of online taxi-hailing by considering the projected taxi demand. The proposed method can help to schedule online taxi-hailing resources in advance.

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