New Method of Materials Flow Calculation for Double-String SLCI Type Cement Plant (Part 2: Suspension Preheater and Calciners)

In many industries, energy auditing is important as the basis for controlling processes and designing additional equipment or modifying an existing plant. However, it requires detailed data of the materials flow, which often cannot be determined easily by direct measurement due to high-temperature limitations. This paper presents the second part of an integrated study to perform energy auditing in a separate line and in-line calciners (SLC-I) type cement plant. The second part of this study, as presented in this paper, focused on the materials flow calculation for eight separate cyclones and two calciners. The least square method was employed for solving the obtained overdetermined system equations. Using the operation data from Part 1 of the study, calculation of the detailed materials flow in each cyclone was executed. The results showed that the separation efficiency of cyclones 1A, 2A, 3A, 4A and 1B, 2B, 3B, 4B was 93.86%, 89.80%, 84.41%, 81.98% and 93.96%, 88.70%, 88.53%, 80.72% respectively and the estimated calcination percentage of kiln feed coming out of the ILC and the SLC was 85.3% and 56.3%, respectively. These values are impossible to be measured directly in the cyclones and calciners during plant operation.

Drone-Assisted Image Processing Scheme Using Frame-Based Location Identification for Crack and Energy Loss Detection in Building Envelopes

This paper presents improved methods to detect cracks and thermal leakage in building envelopes using unmanned aerial vehicles (UAV) (i.e., drones) with video camcorders and/or infrared cameras. Three widely used contour detectors of Sobel, Laplacian, and Canny algorithms were compared to find a better solution with low computational overhead. Furthermore, a scheme using frame-based location identification was developed to effectively utilize the existing approach by finding the current location of the drone-assisted image frame. The results showed a simplified drone-assisted scheme along with automation, higher accuracy, and better speed while using lower battery energy. Furthermore, this paper found that the cost-effective drone with the attached equipment generated accurate results without using an expensive drone. The new scheme of this paper will contribute to automated anomaly detection, energy auditing, and commissioning for sustainably built environments.

ENERGY RETROFITS FOR IMPROVING ENERGY EFFICIENCY IN BUILDINGS: A REVIEW OF HVAC AND LIGHTING SYSTEMS

It has been observed that inefficient buildings consume three to five times more energy than efficient buildings. Subsequently, improving the Energy Efficiency (EE) of existing buildings, which account for a significant portion of the energy consumption of the building sector, has become a top priority. Also, Heating, Ventilation, and Air Conditioning (HVAC) and lighting systems typically account for three-quarters of a building's energy consumption. Hence, focus on the energy efficiency improvements associated with these subsystems is entailed to optimise the energy use of buildings in comparison to other energy consumers. Energy Retrofit (ER) is defined as the main approach in improving the energy efficiency of buildings to achieve energy reduction goals. Nevertheless, there is a general lack of awareness regarding ER. Thus, the purpose of this article is to bridge this research gap by critically reviewing the applicable literature on ER. The paper first analysed the role of retrofits in buildings concerning optimising energy performance. The paper also discusses the implementation process of ER, which includes five steps viz. pre-retrofit survey, energy auditing, and performance assessment, identification of suitable and feasible retrofit options, site implementation and commissioning, and validation and verification. Further, different types of ER applicable to HVAC and lighting systems are discussed. In their endeavor to enhance the EE of existing buildings, practitioners could apply the findings of this study, as a basis to understand the available ER types and as a measure to gauge the efficiency of existing buildings, which will facilitate effective decision-making.

Techno Economic Feasibility Analysis of Solar PV System in Jammu: A Case Study

Renewable sources of energy and related technologies are essential to the generation of energy worldwide. The photovoltaic (PV) is one of the renewable power technologies that support household electricity use. No prior research has studied the sustainability of the off-grid energy generation system in Jammu, India despite the potential of solar photovoltaics and significant amounts of global sun radiation in an area. The present work shown in the chapter is to calculate the residential load of the Patyari Kaltan situated in district Samba of Jammu by energy auditing. The NASA Surface Meteorology is used for the solar resource informationof selected village. The primary sources of electricity generation are fossil fuels. Recently, the energy demand and availability deficit has worsened due to the huge population and fossil fuels cannot fulfill huge energy requirement. Meanwhile they have negative impacts on the environment as well. Therefore, renewable energy offers suitable energy way out to the residents living in remote areas and in the areas near to Borders. In this paper the main aim is to examine the feasibility of solar-battery hybrid energy system to fulfill electrical demand of a residential area in a rural region in Jammu. The research shows that the cost of construction of the project can be repaid or recovered within 1 year 6 months. To accomplish the target, 214 solar panels of 325 watt are estimated to satisfy the demand 100 percent at all times. The findings of this modeling reveal that the off-grid PV system is both technical and economically viable for power generation; they may serve as a model for the successful development of the system for practical use. Furthermore, the model can promote assistance mechanisms for players in the renewable industry to introduce a PV system in residential buildings.

Conceptual Framework for Enhancing Energy Disaggregation Using Crowd and Context

This paper explores existing electrical disaggregation workflows and how they can be augmented with context awareness through datasets. The goal of energy disaggregation is to educate consumers on their energy usage. Additional benefits in automation, security, and energy auditing can be realized through disaggregation. The use of statistical analysis provides specific device consumption information that can be actioned to conserve energy in a directed and methodical manner. The current landscape of disaggregation is a complex workflow involving algorithms that detect, analyze and reveal consumption patterns. Disaggregation workflows involve the acquisition of energy signals for an entire building, refining readings, detecting events, extracting features, and classification. Each step in the workflow impacts the accuracy in which individual devices are detected. Disaggregation workflows may incorporate device usage and weather patterns to improve accuracy, but crowdsourcing signatures and the incorporation of datasets that allow for context awareness are strategies yet to be adopted.

Conceptual Framework for Enhancing Energy Disaggregation Using Crowd and Context

This paper explores existing electrical disaggregation workflows and how they can be augmented with context awareness through datasets. The goal of energy disaggregation is to educate consumers on their energy usage. Additional benefits in automation, security, and energy auditing can be realized through disaggregation. The use of statistical analysis provides specific device consumption information that can be actioned to conserve energy in a directed and methodical manner. The current landscape of disaggregation is a complex workflow involving algorithms that detect, analyze and reveal consumption patterns. Disaggregation workflows involve the acquisition of energy signals for an entire building, refining readings, detecting events, extracting features, and classification. Each step in the workflow impacts the accuracy in which individual devices are detected. Disaggregation workflows may incorporate device usage and weather patterns to improve accuracy, but crowdsourcing signatures and the incorporation of datasets that allow for context awareness are strategies yet to be adopted.

Automated Residential Energy Audits Using a Smart WiFi Thermostat-Enabled Data Mining Approach

Smart WiFi thermostats, when they first reached the market, were touted as a means for achieving substantial heating and cooling energy cost savings. These savings did not materialize until additional features, such as geofencing, were added. Today, average savings from these thermostats of 10–12% in heating and 15% in cooling for a single-family residence have been reported. This research aims to demonstrate additional potential benefit of these thermostats, namely as a potential instrument for conducting virtual energy audits on residences. In this study, archived smart WiFi thermostat measured temperature data in the form of a power spectrum, corresponding historical weather and energy consumption data, building geometry characteristics, and occupancy data were integrated in order to train a machine learning model to predict attic and wall R-Values, furnace efficiency, and air conditioning seasonal energy efficiency ratio (SEER), all of which were known for all residences in this study. The developed model was validated on residences not used for model development. Validation R-squared values of 0.9408, 0.9421, 0.9536, and 0.9053 for predicting attic and wall R-values, furnace efficiency, and AC SEER, respectively, were realized. This research demonstrates promise for low-cost data-based energy auditing of residences reliant upon smart WiFi thermostats.