NOISE CONTROL OF AIR CONDITIONING COOLING TOWERS

Legionnaire’s disease and COVID-19: Could Legionella be acting in conjunction with the Corona virus to aggravate the COVID-19 disease?

10.31219/osf.io/4xach ◽

2020 ◽

Author(s):

Andrew John PENDERY

Keyword(s):

Drinking Water ◽

Air Conditioning ◽

Water Systems ◽

Contaminated Water ◽

Cooling Towers ◽

Age Group ◽

Corona Virus ◽

The Usa ◽

Legionnaire's Disease ◽

Legionnaire’S Disease

There are some striking similarities between Legionnaire’s disease and COVID-19. Thesymptoms, age group and sex at risk are identical. The geographical distribution of both diseases is similar in Europe overall, and within the USA, France and Italy. The environmental distributions are also similar. However Legionnaire’s disease is caused by Legionella bacteria while COVID-19 is caused by the Corona virus. Whereas COVID-19 is contagious, Legionnaire’s disease is environmental. Legionella bacteria are commonly found in drinking water systems and near air conditioning cooling towers. Legionnaire’sdisease is caught by inhaling contaminated water droplets. The Legionella bacteria does not spread person to person and only causes disease if it enters the lungs.Could the Corona virus be making it easier for Legionella bacteria to enter the lungs?

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Noise Control in Heating, Ventilating, and Air Conditioning Systems

Noise and Vibration Control Engineering ◽

10.1002/9780470172568.ch17 ◽

2007 ◽

pp. 685-719

Author(s):

Alan T. Fry ◽

Douglas H. Sturz

Keyword(s):

Air Conditioning ◽

Noise Control ◽

Air Conditioning Systems

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Paper 6: Some Basic Considerations in the Practice of Heating, Ventilating and Air-Conditioning

Proceedings of the Institution of Mechanical Engineers Conference Proceedings ◽

10.1243/pime_conf_1967_182_158_02 ◽

1967 ◽

Vol 182 (5) ◽

pp. 129-137

Author(s):

W. H. Eccleston

Keyword(s):

Air Conditioning ◽

Response Rate ◽

Water Systems ◽

Thermal Capacity ◽

Hot Water ◽

Space Heating ◽

Noise Attenuation ◽

Cooling Towers ◽

Steam Generation ◽

Fuel Savings

This paper covers some of the basic considerations associated with the practice of heating, ventilating and air-conditioning in temperate climates. A diagrammatic representation of heat loss and gain for a room appears to provide a key to more accurate forecasting of fuel consumption for whole buildings. Further, the smaller the thermal capacity of the system and, therefore, the quicker the response rate, the larger is the possible scope for fuel savings. As far as space heating is concerned water systems are classified and there is reference to the more commonly used heat emitters and some of their characteristics. There is some reference to boiler power both for hot-water heating and steam generation. Ventilation is discussed in the context of terminal points; there is also a brief reference to noise attenuation in ducts and to balancing of systems. Air-conditioning is defined and the better known distribution methods are classified. Packaged water chillers are briefly examined and there are some suggestions regarding ‘mixing-units’. In addition there are some comments on cooling towers. In conclusion there is a plea for standardization and in this particular instance reference is made to specifications for mechanical services works.

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Machine Learning Approaches for Accurate Prediction of Relative Humidity based on Temperature and Wet-Bulb Depression

10.20944/preprints202002.0075.v2 ◽

2020 ◽

Author(s):

Mahdi Ghadiri ◽

Azam Marjani ◽

Samira Mohammadinia ◽

Manouchehr Shokri

Keyword(s):

Relative Humidity ◽

Statistical Learning ◽

Air Conditioning ◽

Fuzzy Inference ◽

Least Square ◽

Support Vector ◽

Cooling Towers ◽

Learning Approaches ◽

Inference System ◽

Data Points

The main parameters for calculation of relative humidity are the wet-bulb depression and dry bulb temperature. In this work, easy-to-used predictive tools based on statistical learning concepts, i.e., the Adaptive Network-Based Fuzzy Inference System (ANFIS) and Least Square Support Vector Machine (LSSVM) are developed for calculating relative humidity in terms of wet bulb depression and dry bulb temperature. To evaluate the aforementioned models, some statistical analyses have been done between the actual and estimated data points. Results obtained from the present models showed their capabilities to calculate relative humidity for divers values of dry bulb temperatures and also wet-bulb depression. The obtained values of MSE and MRE were 0.132 and 0.931, 0.193 and 1.291 for the LSSVM and ANFIS approaches respectively. These developed tools are user-friend and can be of massive value for scientists especially, those dealing with air conditioning and wet cooling towers systems to have a noble check of the relative humidity in terms of wet bulb depression and dry bulb temperatures.

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