Cooling load forecasting-based predictive optimisation for chiller plants

Extensive electric power is required to maintain indoor thermal comfort using heating, ventilation and air conditioning (HVAC) systems, of which, water-cooled chiller plants consume more than 50% of the total electric power. To improve energy efficiency, supervisory optimisation control can be adopted. The controlled variables are usually optimised according to instant building cooling load and ambient wet bulb air temperature at regular time intervals. In this way, the energy efficiency of chiller plants has been improved. However, with an inherent assumption that the instant building cooling load and ambient wet bulb temperature remain constant in the coming time interval, the energy efficiency potential has not been fully realised, especially when cooling loads vary suddenly and extremely. To solve this problem, a cooling load forecasting-based predictive optimisation method is proposed. Instead of minimising the instant system power according to the instant building cooling load and ambient wet bulb temperature, the controlled variables are derived to minimise the sum of the instant system power and one-time-step-ahead future system power according to both instant and forecasted future building cooling loads. With this method, the energy efficiency potential of a chiller plant can be further improved without shortening the operation time interval. 80% redundant energy consumption has been reduced for the sample chiller plant; energy can be saved for chiller plants that work for years. The evaluation on the effect of cooling load forecasting accuracy turns out that the more accurate the forecasts are, the more redundant energy consumption can be reduced

An integrated model for the design of air-cooled chiller plants for commercial buildings

Cooling load calculation is the first step in designing the air-conditioning system of a building. The calculated cooling capacity with appropriate buffer is then used to select the number and size of chillers in the system. Nþ 1 is a common formula used by designers to size the chiller plants in Hong Kong buildings, where N is the actual number of chillers required and 1 is a redundant chiller provided to ensure reliability. This paper reviews the problem of excess capacity and discusses the risk exposure of chiller systems without redundant chillers. The cooling load profiles of the chiller plants of four medium-sized commercial buildings inHong Kongare reviewed. The risk exposure of chiller systemswithout redundant chillers can be minimized byapplying risk-based preventive maintenance. The just-in-demand design reduces capital cost of the building and frees up funds for continuous energy measurement and improving the energy efficiency of chiller plant systems. This paper presents a model for designing chiller plants that improves the energy efficiency of the plant in a cost effective and thoughtful manner. It is designed with consideration of the life cycle of the plant and real-time continuous commissioning, monitoring, measurement, comparison and execution for better energy manageme

Thermal, crystalline and mechanical properties of flame retarded poly(lactic acid) with a PBO-like small molecule - phenylphosphonic bis(2-aminobenzothiazole)

202209 bckwAccepted ManuscriptRGCPublishe

Cooling load forecasting-based predictive optimisation for chiller plants

Extensive electric power is required to maintain indoor thermal comfort using heating, ventilation and air conditioning (HVAC) systems, of which, water-cooled chiller plants consume more than 50% of the total electric power. To improve energy efficiency, supervisory optimisation control can be adopted. The controlled variables are usually optimised according to instant building cooling load and ambient wet bulb air temperature at regular time intervals. In this way, the energy efficiency of chiller plants has been improved. However, with an inherent assumption that the instant building cooling load and ambient wet bulb temperature remain constant in the coming time interval, the energy efficiency potential has not been fully realised, especially when cooling loads vary suddenly and extremely. To solve this problem, a cooling load forecasting-based predictive optimisation method is proposed. Instead of minimising the instant system power according to the instant building cooling load and ambient wet bulb temperature, the controlled variables are derived to minimise the sum of the instant system power and one-time-step-ahead future system power according to both instant and forecasted future building cooling loads. With this method, the energy efficiency potential of a chiller plant can be further improved without shortening the operation time interval. 80% redundant energy consumption has been reduced for the sample chiller plant; energy can be saved for chiller plants that work for years. The evaluation on the effect of cooling load forecasting accuracy turns out that the more accurate the forecasts are, the more redundant energy consumption can be reduced

Facile flame retardant finishing of cotton fabric with hydrated sodium metaborate

202209 bckwAccepted ManuscriptRGCPublishe