Automatic Continuous Online Monitoring and Control of Polymerization Reactions and Related Methods

Author(s):  
Wayne F. Reed
Author(s):  
Adamu Yebi ◽  
Beshah Ayalew ◽  
Satadru Dey

This article discusses the challenges of non-intrusive state measurement for the purposes of online monitoring and control of Ultraviolet (UV) curing processes. It then proposes a two-step observer design scheme involving the estimation of distributed temperature from boundary sensing cascaded with nonlinear cure state observers. For the temperature observer, backstepping techniques are applied to derive the observer partial differential equations along with the gain kernels. For subsequent cure state estimation, a nonlinear observer is derived along with analysis of its convergence characteristics. While illustrative simulation results are included for a composite laminate curing application, it is apparent that the approach can also be adopted for other UV processing applications in advanced manufacturing.


2015 ◽  
Vol 785 ◽  
pp. 236-240
Author(s):  
Tan Jen Hau ◽  
N.M. Nor ◽  
T. Ibrahim ◽  
H. Daud

A new method for monitoring and control of domestic distribution box is proposed and developed for automated recovery of power continuity during interruption. The system automatically test each of the sockets to determine the source of the failure and isolate them. The data of the modified connection will be sent to the client through a server, wirelessly to notify the user the modifications made. Parallel processing via multi-threading in the server are used to increment the upper limit of TCP transmission's throughput. Multiple SQLite database are used by multiple threads for parallel storage of data to increase performance.


Water ◽  
2015 ◽  
Vol 7 (11) ◽  
pp. 6574-6597 ◽  
Author(s):  
Harsha Ratnaweera ◽  
Joachim Fettig

2019 ◽  
Vol 29 (4) ◽  
pp. 587-602 ◽  
Author(s):  
Hao-Cheng Zhu ◽  
Chuck Wah Yu ◽  
Shi-Jie Cao

Dynamic optimal airflow ventilation can have a great impact on the indoor air distribution and pollutant removal to improve the indoor air quality while saving energy. An online monitoring and control ventilation system has been developed and evaluated using fast prediction models and micro-control. An environmental chamber (1.8 m3) was used for the evaluation to monitor the CO2 dispersion under different air change rates and air speed. Specifically, an artificial neural network model based on a low-dimensional linear ventilation model was introduced and validated to provide environmental control and rapid prediction of pollutant concentration distribution in the indoor environment, which can save computing time and significantly enhance energy saving efficiency up to 16–47%. The validation was carried out by comparison with measurement data of the chamber experiment. An induction system was applied to locate and monitor the personnel in the office due to pollution that are generated by people. A ZigBee wireless module would transmit location information of pollutant source (i.e. CO2 generated by occupants) and to determine the optimal ventilation mode based on ventilation assessment to achieve automatic control of indoor air quality to ensure the wellbeing of occupants while saving energy.


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