Our study was carried out to assess the level of noise generated and ground vibrations induced during blasting operations at the Ewekoro limestone quarry in Nigeria.To achieve this objective,vibro monitor equipment wa...Our study was carried out to assess the level of noise generated and ground vibrations induced during blasting operations at the Ewekoro limestone quarry in Nigeria.To achieve this objective,vibro monitor equipment was used to take readings related to noise generated and ground vibrations during all blasting operations that took place in the quarry for a period of one month.As well,a digital camera was used to take photographs of residential structures within villages near the quarry.The results obtained indicate that the ground vibration readings fall between 0.5 mm/s and 2.1 mm/s and the noise generated during the blasting operations between 82 dB and 89 dB.These readings when compared with the limits set by FEPA(Federal Environmental Protection Agency) of 5.0 mm/s and 150 dB) all fall within the permissible limits.However the photographs of most structures near the quarry reveal cracks and dilapidated building walls.Recommendations are made on how to sustain and improve current blasting techniques.展开更多
An iterative learning model predictive control （ILMPC） technique is applied to a class of continuous/batch processes. Such processes are characterized by the operations of batch processes generating periodic strong ...An iterative learning model predictive control （ILMPC） technique is applied to a class of continuous/batch processes. Such processes are characterized by the operations of batch processes generating periodic strong disturbances to the continuous processes and traditional regulatory controllers are unable to eliminate these periodic disturbances. ILMPC integrates the feature of iterative learning control （ILC） handling repetitive signal and the flexibility of model predictive control （MPC）. By on-line monitoring the operation status of batch processes, an event-driven iterative learning algorithm for batch repetitive disturbances is initiated and the soft constraints are adjusted timely as the feasible region is away from the desired operating zone. The results of an industrial application show that the proposed ILMPC method is effective for a class of continuous/batch processes.展开更多
文摘Our study was carried out to assess the level of noise generated and ground vibrations induced during blasting operations at the Ewekoro limestone quarry in Nigeria.To achieve this objective,vibro monitor equipment was used to take readings related to noise generated and ground vibrations during all blasting operations that took place in the quarry for a period of one month.As well,a digital camera was used to take photographs of residential structures within villages near the quarry.The results obtained indicate that the ground vibration readings fall between 0.5 mm/s and 2.1 mm/s and the noise generated during the blasting operations between 82 dB and 89 dB.These readings when compared with the limits set by FEPA(Federal Environmental Protection Agency) of 5.0 mm/s and 150 dB) all fall within the permissible limits.However the photographs of most structures near the quarry reveal cracks and dilapidated building walls.Recommendations are made on how to sustain and improve current blasting techniques.
基金Supported by the National Creative Research Groups Science Foundation of China （60721062） and the National High Technology Research and Development Program of China （2007AA04Z162）.
文摘An iterative learning model predictive control （ILMPC） technique is applied to a class of continuous/batch processes. Such processes are characterized by the operations of batch processes generating periodic strong disturbances to the continuous processes and traditional regulatory controllers are unable to eliminate these periodic disturbances. ILMPC integrates the feature of iterative learning control （ILC） handling repetitive signal and the flexibility of model predictive control （MPC）. By on-line monitoring the operation status of batch processes, an event-driven iterative learning algorithm for batch repetitive disturbances is initiated and the soft constraints are adjusted timely as the feasible region is away from the desired operating zone. The results of an industrial application show that the proposed ILMPC method is effective for a class of continuous/batch processes.
