Indiana University-Purdue University Indianapolis (IUPUI) / The compressed air system is widely used throughout the industry. Air compressors are one of the most costly systems to operate in industrial plants in terms of energy consumption. Therefore, it becomes one of the primary targets when it comes to electrical energy and load management practices. Load forecasting is the first step in developing energy management systems both on the supply and user side. A comprehensive literature review has been conducted, and there was a need to study if predicting compressed air system’s load is a possibility. System’s load profile will be valuable to the industry practitioners as well as related software providers in developing better practice and tools for load management and look-ahead scheduling programs. Feed forward neural networks (FFNN) and long short-term memory (LSTM) techniques have been used to perform 15 minutes ahead prediction. Three cases of different sizes and control methods have been studied. The results proved the possibility of the forecast. In this study two control methods have been developed by using the prediction. The first control method is designed for variable speed driven air compressors. The goal was to decrease the maximum electrical load for the air compressor by using the system's full operational capabilities and the air receiver tank. This goal has been achieved by optimizing the system operation and developing a practical control method. The results can be used to decrease the maximum electrical load consumed by the system as well as assuring the sufficient air for the users during the peak compressed air demand by users. This method can also prevent backup or secondary systems from running during the peak compressed air demand which can result in more energy and demand savings. Load management plays a pivotal role and developing maximum load reduction methods by users can result in more sustainability as well as the cost reduction for developing sustainable energy production sources. The last part of this research is concentrated on reducing the energy consumed by load/unload controlled air compressors. Two novel control methods have been introduced. One method uses the prediction as input, and the other one doesn't require prediction. Both of them resulted in energy consumption reduction by increasing the off period with the same compressed air output or in other words without sacrificing the required compressed air needed for production. / 2019-12-05

<div>The compressed air system is widely used throughout the industry. Air compressors are one of the most costly systems to operate in industrial plants in therms of energy consumption. Therefore, it becomes one of the primary target when it comes to electrical energy and load management practices. Load forecasting is the first step in developing energy management systems both on the supply and user side. A comprehensive literature review has been conducted, and there was a need to study if predicting compressed air system’s load is a possibility. </div><div><br></div><div>System’s load profile will be valuable to the industry practitioners as well as related software providers in developing better practice and tools for load management and look-ahead scheduling programs. Feed forward neural networks (FFNN) and long short-term memory (LSTM) techniques have been used to perform 15 minutes ahead prediction. Three cases of different sizes and control methods have been studied. The results proved the possibility of the forecast. In this study two control methods have been developed by using the prediction. The first control method is designed for variable speed driven air compressors. The goal was to decrease the maximum electrical load for the air compressor by using the system's full operational capabilities and the air receiver tank. This goal has been achieved by optimizing the system operation and developing a practical control method. The results can be used to decrease the maximum electrical load consumed by the system as well as assuring the sufficient air for the users during the peak compressed air demand by users. This method can also prevent backup or secondary systems from running during the peak compressed air demand which can result in more energy and demand savings. Load management plays a pivotal role and developing maximum load reduction methods by users can result in more sustainability as well as the cost reduction for developing sustainable energy production sources. The last part of this research is concentrated on reducing the energy consumed by load/unload controlled air compressors. Two novel control methods have been introduced. One method uses the prediction as input, and the other one doesn't require prediction. Both of them resulted in energy consumption reduction by increasing the off period with the same compressed air output or in other words without sacrificing the required compressed air needed for production.</div><div><br></div>

This research focused on evaluating and comparing the perceptions, water-use behaviour, water conservation awareness and overall water stewardship of participants residing in Hammanskraal and Atteridgeville who have experienced intermittent water supply in their domestic households – due to either water contamination incidents caused by dilapidated infrastructure or water restrictions implemented by the City of Tshwane Metropolitan Municipality during the 2016–2017 drought in the Gauteng Province. The research concluded that the municipality needs to implement proactive water conservation awareness initiatives on an ongoing basis to reduce high water demands and to create a culture of water stewardship, especially in Atteridgeville. Transparent communication is also required from the municipality to instil the necessary trust among the public. It is recommended that the municipality attends to water leaks and ongoing complaints from the public timeously to reduce the current apathy from the public against reporting water-related issues and to ultimately ensure compliance to water restrictions. / Hierdie navorsing fokus op die evaluering en vergelyking van deelnemers wat in Hammanskraal en Atteridgeville woon se persepsies, waterverbruiksgedrag, waterbewaringsbewustheid en algehele waterrentmeesterskap, wat onderbroke watervoorsiening in hulle huishoudings ervaar het – as gevolg van waterbesoedelingsvoorvalle wat deur vervalle infrastruktuur veroorsaak is en waterbeperkings wat deur die Stad Tshwane Metropolitaanse Munisipaliteit gedurende die 2016 tot 2017-droogte in Gauteng ingestel is. Die navorsing het tot die gevolgtrekking gekom dat die munisipaliteit proaktiewe waterbewaringsbewustheidsinisiatiewe op ’n deurlopende grondslag moet implementeer om hoë wateraanvraag te verminder en ’n kultuur van waterrentmeesterskap, veral in Atteridgeville, te skep. Deursigtige kommunikasie word ook van die munisipaliteit vereis om die nodige vertroue by die publiek te kweek. Daar word aanbeveel dat die munisipaliteit betyds aandag aan waterlekkasies en deurlopende klagtes van die publiek sal gee om die huidige onverskilligheid van die publiek by die aanmeld van waterverwante aangeleenthede te verminder en om uiteindelik te verseker dat die publiek die waterbeperkings eerbiedig. / Patlisiso ena e ne e tsepame hodima ho lekola le ho bapisa maikutlo, boitshwaro ba tshebediso ya metsi, tsebo ka poloko ya metsi le tlhokomelo e akaretsang ya metsi ke bankakarolo ba dulang Hammanskraal le Atteridgeville ba bileng le phepelo ya metsi e kgaohang malapeng a bona – e ka ba ka lebaka la diketsahalo tsa tshilafatso ya metsi e bakilweng ke dipeipi tse senyehileng kapa ho kgaolwa ha metsi ho kentsweng tshebetsong ke Masepala wa Motsemoholo wa Metropolitan wa Tshwane nakong ya komello ya 2016–2017 porofenseng ya Gauteng. Patlisiso e fumane hore masepala o hloka ho kenya tshebetsong matsholo a ho atisa tsebo ka poloko ya metsi ka mokgwa o tswellang e le ho fokotsa tlhokeho e phahameng ya metsi le ho theha ditlwaelo tsa tlhokomelo ya metsi, haholo ho la Atteridgeville. Ho boetse ho hlokeha puisano e hlakileng e nang le ponaletso ho tswa ho masepala e le hore setjhaba se be le tshepo ho ona. Ho kgothaletswa hore masepala a sebetsane le diketsahalo tsa ho dutla ha metsi le ditletlebo tse tswellang tse tswang ho setjhaba ka potlako e le ho fokotsa maikutlo a ho tsotelle a tswang ho setjhaba mabapi le ho tlaleha mathata a amanang le metsi le ho netefatsa hore batho ba latela melawana ya phokotso ya metsi. / Geography / M. Sc. (Geography)