Abstract
Indoor air quality in pharmaceutical industry plays a vital role in the production and storing of medicine. Stable indoor environment including favorable temperature, humidity, air flow and number of microorganisms requires consistent monitoring. This paper aimed to develop a fuzzy logic-based intelligent ventilation system to control the indoor air quality in pharmaceutical sites. Specifically, in the proposed fuzzy inference system, the ventilation system can control the air flow and quality in accordance with the indoor temperature, humidity, air flow and microorganisms in the air. The MATLAB® fuzzy logic toolbox was used to simulate the performance of the fuzzy inference system. The results show that the efficiency of the system can be improved by manipulating the input-output parameters according to the user’s demands. Compared with conventional heating, ventilation and air-conditioning (HVAC) systems, the proposed ventilation system has the additional feature of the existence of microorganisms, which is a crucial criterion of indoor air quality in pharmaceutical laboratories.
Highlights
The ventilation system plays an important role in the pharmaceutical industry
Ventilation and air-conditioning (HVAC) systems, the proposed ventilation system has the additional feature of the existence of microorganisms, which is a crucial criterion of indoor air quality in pharmaceutical laboratories
As shown in the table, the humidity has a lower effect on controlling the air conditioning system and fan speed but a marked effect on the mode of the system
Summary
The ventilation system plays an important role in the pharmaceutical industry. Without a ventilation system, the quality of medicines may degrade due to fluctuations in the number of microorganisms and an increase in the temperature of the indoor environment. A previous study [4] investigated the use of fuzzy logic-based patient monitoring and alarming systems by anaesthesiologists during highly invasive surgical procedures. In this sequel, [6], [7] investigated fuzzy logic-based controller design for the weaning process for ventilated patients. An auto-tuning receding-horizon optimization method was previously proposed [14] to synthesize a proportional-integralderivative (PID)-type controller for air-handling units (AHUs) This algorithm comprises two levels of control: the lower level uses a conventional PID controller to obtain an acceptable, but not necessarily optimal, performance, and the higher level provides optimal low-level controller parameters through minimization of the generalized predictive control criterion. The MATLAB® fuzzy logic toolbox was used to simulate the inference system
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