The impact of occupancy on baseline building energy modelling performance

For promoting energy efficiency practices in the building sector, energy conservation measures are of utmost importance. The energy conservation measures implemented through Energy performance contracting (EPC) are predominantly linked with the performance of baseline building energy models. While occupants are recognized as one of the most important driving factors of energy use in buildings, current research has failed to identify if building occupancy rate can be an influential independent variable to predict baseline energy use in buildings. This research aims to identify the influence of considering occupancy rate as an explanatory variable on the modelling performance of baseline building energy. Six multivariate machine learning approaches (e.g., linear regression, regression trees, ensembles of trees, Gaussian Process, support vector machine, nonlinear Autoregressive Exogenous model (NARX)) and one univariate (e.g., reformed ten-parameter change point model) inverse modelling approach were employed in the baseline model development process of building heating and cooling energy use and electricity. The specified multivariate baseline modelling approaches were investigated to better isolate the impact of occupancy on building energy performance. NARX outperformed other baseline modelling approaches in terms of model predictive accuracy and data fitting capabilities. On the contrary, the proposed adapted change point model demonstrates the capability of providing operational insight into the case study building. The hourly fifteen-month worth of energy use data used in baseline models was extracted from the building management system (BMS) server of a real case study building. The prediction period was defined as the most recent six months of the available data representing the COVID lockdown period. The models were trained using the nine-month worth of data that immediately preceded the prediction period. The arrangement of different input parameters selected by a forward sequential feature selection approach was considered an important step to identify the influence of individual parameters on baseline energy use. The influence of occupancy on the accuracy of baseline models was quantitively evaluated from this analysis. The results show that baseline model performance slightly improves when occupancy data are considered as an explanatory variable. However, occupancy data can significantly influence the performance of a baseline energy use model in an occupant-centric building. The assessment of hourly energy data and associated occupancy data for the case study building indicates the necessity of implementing occupant-centric control strategies to improve its energy performance

Tribological behavior of dual and triple particle size SiC reinforced Al0MMCs: a comparative study

Purpose- The aim is to study the tribological behavior of dual particle size (DPS)and triple particle size (TPS) SiC reinforced aluminium allloy based metal matrix composites - MMCs (Al/SiCp MMC)SiCp/Al-MMC). Design /methodology /approach- Al-MMCs were prepared using 20 vol% SiC reinforcement into aluminum metal matrix and developed using a stir casting process. Stir casting is a primary process of composite production whereby the reinforcement ingredient material is incorporated into the molten metal by stirring. The triple particle size (TPS) composite consist of SiC of three different sizes viz., coarse, intermediate and fine. The solution heat treatment was done on as cast (AC) composite at 540 0C for four hours followed by precipitation treatment. The wear test was carried out using a pin-on-disc type tribo-test machine under dry sliding condition. A mathematical analysis was also done for power factor values based on wear and friction results. The wear morphology of the damaged surface was also studied using optical microscope and scanning electron microscope (SEM) in this investigation. Findings- The test results showed that heat treated composite exhibited better wear resistance properties compared to as cast composite. It is anticipated that heat treatment could be an effective method of optimizing the wear resistance properties of the developed Al-MMC material

Wear behavior of as-cast and heat treated triple particle size SiC reinforced aluminium metal matrix composites

Purpose – The purpose of this paper is to study the wear behavior of as-cast (AC) and heat treated (HT) triple particle size (TPS) silicon carbide (SiC) reinforced aluminum alloy-based metal matrix composites (SiCp/Al-MMC). Design/methodology/approach – Al-MMCs were prepared using 20?vol.% SiC reinforcement into aluminum metal matrix and developed using a stir casting process. Stir casting is a primary process of composite production whereby the reinforcement ingredient material is incorporated into the molten metal by stirring. The TPS composite consist of SiC of three different sizes viz., coarse, intermediate, and fine. The solution heat treatment was done on AC composite at 540°C for 4?h followed by precipitation treatment. The wear test was carried out using a pin-on-disc type tribo-test machine under dry sliding condition. A mathematical analysis was also done for power factor values based on wear and friction results. The wear morphology of the damaged surface was also studied using optical microscope and scanning electron microscope (SEM) in this investigation. Findings – The test results showed that HT composite exhibited better wear resistance properties compared to AC composite. It is anticipated that heat treatment could be an effective method of optimizing the wear resistance properties of the developed Al-MMC material. Practical implications – This paper provides a way to enhance the wear behavior of automotive tribo-components such as brake rotor (disc and drum), brake pad, piston cylinder, etc. Originality/value – This paper compares the wear behavior of AC and HT TPS reinforced Al-MMC material under dry sliding condition

Identifying and Prioritizing the Performance Criteria of Denim Washing Industry in Bangladesh Using Analytic Hierarchy Process

In the midst of the negative growth of textile and RMG industries, the mother industry of Bangladesh, the denim industry, has been doing its part incessantly. While the prospect of the denim industry looks promising from the last few years, the factors that drive this industry forward remain unanswered. Among the process chain of denim manufacturing, most value addition occurs in denim washing. This paper focuses on identifying and prioritizing the performance criteria of the denim washing sector in particular. In this context, export-oriented denim washing factories are chosen and the identified criteria are evaluated by using Analytical Hierarchy Process (AHP). This study is qualitative in nature and the secondary data about the factors were collected initially through review of previous literature, magazines, books, and newspapers. A structured questionnaire was developed to collect data from 35 factories. The results of the study show that cost, time, quality, and flexibility are the critical factors for success. The findings also seem to be consistent in general in regard to the test results, and it provides insight for improvement in the denim washing industry of Bangladesh