On-demand ventilation and energy conservation of industrial exhaust systems based on stochastic modeling

Exhaust ventilation systems have been widely used in factories with multiple heat or pollutant sources. However, conventional “static” designs that fail to consider overlapping terminal ventilation demands result in oversized exhaust systems and high energy consumption. In this work, a novel approach was proposed to calculate dynamic exhaust demand based on stochastic modeling. The developed model could obtain the probability distribution of ventilation demand by adopting the Monte Carlo simulation and determine the optimized designed exhaust rate of the system. A case study showed that the designed exhaust rate of a 20-machine vulcanizing line could be reduced by 45% after the coincidence factor was considered as 0.55. Moreover, an annual energy conservation of 51,465 kWh was possible. The influence of various factors, including exhaust time, cycle time, the number of machines, the number of workers and operating time, was discussed. Findings showed that the coincidence factor was positively proportional to exhaust time but negatively associated with cycle time and the number of machines. In addition, a machine-startup schedule was introduced to optimize the operating schedule, which further reduced the exhaust rate.