A case study on rock damage prediction and control method for underground tunnels subjected to adjacent excavation blasting

At the moment, many engineer-to-order manufacturers are under pressure, the overcapacity in many sectors erodes prices and many companies, especially in Europe have gone into recent years in bankruptcy. Due to the increasing competition as well as the new customer requirements, the internal processes of an ETO company play an essential role in order to achieve a unique selling proposition (USP). Therefore this paper exposes how the production planning and control of an engineer-to-order manufacturer can be designed in order to increase its OTD (order-to-delivery) rate as well as decrease the WIP (work-in-progress) and the production lead times. To prove the optimized planning logic, it was applied in a simulation case study and based on the results; the conclusions about its potential are derived.

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Application of Numerical Modelling and Genetic Programming in Hydrocarbon Seepage Prediction and Control for Crude Oil Storage Unlined Rock Caverns

Geofluids ◽

10.1155/2017/6803294 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Ebrahim Ghotbi Ravandi ◽

Reza Rahmannejad ◽

Saeed Karimi-Nasab ◽

Amir Sarrafi

Keyword(s):

Genetic Programming ◽

Numerical Modelling ◽

Crude Oil ◽

Control Method ◽

Effective Parameters ◽

Two Phase ◽

Seepage Control ◽

Oil Storage ◽

Prediction And Control ◽

And Control

Seepage control is a prerequisite for hydrocarbon storage in unlined rock caverns (URCs) where the seepage of stored products to the surrounding host rock and groundwater can cause serious environmental and financial problems. Practically seepage control is performed by permeability and hydrodynamic control methods. This paper employs numerical modelling and genetic programming (GP) for the purpose of seepage prediction and control method determination for the crude oil storage URCs based on the effective parameters including hydrogeologic characteristic of the rock and physicochemical properties of the hydrocarbons. Several levels for each parameter were considered and all the possible scenarios were modelled numerically for the two-phase mixture model formulation. The corresponding seepage values were evaluated to be used as genetic programming data base to generate representative equations for the hydrocarbon seepage value. The coefficients of determination (R2) and relative percent errors of the proposed equations show their ability in the seepage prediction and permeability or hydrodynamic control method determination and design. The results can be used for crude oil storage URCs worldwide.

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Silicon microgyroscope temperature prediction and control system based on BP neural network and Fuzzy-PID control method

Measurement Science and Technology ◽

10.1088/0957-0233/26/2/025101 ◽

2015 ◽

Vol 26 (2) ◽

pp. 025101 ◽

Cited By ~ 18

Author(s):

Dunzhu Xia ◽

Lun Kong ◽

Yiwei Hu ◽

Peizhen Ni

Keyword(s):

Neural Network ◽

Control System ◽

Bp Neural Network ◽

Pid Control ◽

Control Method ◽

Fuzzy Pid ◽

Fuzzy Pid Control ◽

Temperature Prediction ◽

Prediction And Control ◽

And Control

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Modeling, Prediction, and Control of Heating Temperature for Tube Billet

Mathematical Problems in Engineering ◽

10.1155/2015/576813 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Yachun Mao ◽

Dong Xiao ◽

Dapeng Niu

Keyword(s):

Control Method ◽

Heating Temperature ◽

Nonlinear Models ◽

Time Lag ◽

Industrial Applications ◽

Billet Temperature ◽

Tube Billet ◽

Prediction And Control ◽

Coupling Characteristics ◽

And Control

Annular furnaces have multivariate, nonlinear, large time lag, and cross coupling characteristics. The prediction and control of the exit temperature of a tube billet are important but difficult. We establish a prediction model for the final temperature of a tube billet through OS-ELM-DRPLS method. We address the complex production characteristics, integrate the advantages of PLS and ELM algorithms in establishing linear and nonlinear models, and consider model update and data lag. Based on the proposed model, we design a prediction control algorithm for tube billet temperature. The algorithm is validated using the practical production data of Baosteel Co., Ltd. Results show that the model achieves the precision required in industrial applications. The temperature of the tube billet can be controlled within the required temperature range through compensation control method.

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