Optimize Energy Usage Research Articles

Coordination of radiant floor and baseboard heating systems: Sequential and simultaneous MPC schemes

Two novel model predictive control (MPC) schemes are proposed in this article for coordinating two different heating systems with fast and slow heating dynamics. The objective is to improve the performance of a slow-reacting heating system in terms of maintaining the indoor operative temperature within predefined bounds while reducing the energy cost. Here, a combination of a hydronic radiant floor heating (RFH) system and electric baseboard (BB) heaters is used for the demonstration. A sequential approach is proposed where separate MPC optimizations are performed sequentially for the RFH and BB heaters, whereas for the simultaneous approach a single MPC optimizes the two heating systems concurrently. The performances of these two cooperative schemes are compared with the base case where the RFH is used as the only heating system. The simultaneous approach results in achieving improved comfort with a 6% reduction in the energy cost compared to the base case. The RFH system, for both the base case and the cooperative setups, uses a configuration incorporating a heat pump and a thermal energy storage (TES) tank for optimal energy usage based on the time-of-use electricity rates.

Quantification of inherent energy resilience of process systems for optimization of energy usage

AbstractThis study shows how inherent energy resilience for process systems can be conceptualized from the perspectives of material resilience. Equations are derived to assess inherent energy resilience properties of constituent process systems of a gas sweetening unit (GSU). A new method is introduced for identifying thermodynamically efficient operating capacity regime for optimization of energy usage in GSU. It is found that the GSU operating capacity range of 80–100% is the most thermodynamically efficient operating capacity regime. It is evaluated that operation of the GSU below 80% of its design capacity will incur higher percentage of wastage of valuable work in spite of spending lesser total work. Process simulator, Pro/II has been utilized for this study.

Energy Packet Networks: an Annotated Bibliography

Since the beginning of the age of the digital-revolution, computer and communication systems have used massive electrical power. As a result, the usage of electrical energy by the Information and Communication Technologies (ICT) has now become a major concern in the research community, so as to find new ways to minimize this overall power consumption. The Energy Packet Network (EPN) model was thus proposed to formulate and optimize the dynamic behavior of energy consumption by the ICT-based infrastructures. In this paper, we present an annotated bibliography of work that has been done regarding the EPN model, to provide a bird’s eye view of this research. A taxonomic representation of EPNs is proposed covering wireless sensor networks, ad hoc networks, clouds, servers, and wired networks. The link between EPNs, Gelenbe Networks and Random Neural Networks is also discussed. The study concludes that the EPN model and paradigm offers a significant research and engineering design direction for the minimization and optimization of energy usage in the ICT ecosystem.

Multi-objective optimization for electric water heater using mixed integer linear programming

Due to the capacity of thermal storage, electric water heater (EWH) is one of the best candidates for demand response programs. However, few attentions are given to the modeling and optimization of EWHs with thermostatically-controlled automatic water mixer (TCAWM). In this paper, differential thermodynamic model is established for EWHs with TCAWM and a piecewise linear approximation method is performed for the nonlinear thermodynamic model. The multi-objective optimization model is established by introducing an index reflecting the comfort degree of users, so that the optimal energy usage of the EWH can be obtained by mixed integer linear programming. Testing examples verify the effectiveness of the proposed method.

Investigating the effect of using PCM in building materials for energy saving: Case study of Sharif Energy Research Institute

AbstractIn order to attain an efficient path into the enhancement of the sustainable conservation and management of energy resources in the buildings and residential sections, efforts by all relevant institutions in the area of energy are essential and primitive. Hence, the development of the energy supply model in the building and the provision of fundamental solutions in this area is rudimentary. The key to any program related to this matter is to boost and extend energy efficiency and energy resource management. According to the global statistics and processed data, the residential sector accounts for 34 percent of the total energy consumption in Iran, which is the highest energy consumption compared to all existing sectors; therefore, immeasurable research has been done on optimizing energy usage in the building sector. One of the most efficient energy optimization methods can be achieved by using energy storage materials in the outer walls of the buildings. In this project, a research‐based residential building is considered as a case to investigate for energy optimization. In the first stage, the building is simulated and the energy consumption of the building is measured. Afterward, with actual measurements, real consumption is approximated and compared with the results of the software. After verifying the software data, the insulation is simulated in the software and the outcomes of using this insulator are investigated to reduce energy consumption. Energy storage materials are considered as a type of phase‐change material. These materials have the same characteristics which is having a different melting point and, by capturing and losing heat at design temperatures, they can help maintain energy inside the building that is how they can be considered as an insulator. Within the appropriate software which is Design‐Builder, miscellaneous types of these materials are available with various features. The final results point out that the use of this material saves up to 30% of the heating energy and about 8% in the cooling load of the building.

Predictive modelling of building energy consumption based on a hybrid nature-inspired optimization algorithm

Overall energy consumption has expanded over the previous decades because of rapid population, urbanization and industrial growth rates. The high demand for energy leads to higher cost per unit of energy, which, can impact on the running costs of commercial and residential dwellings. Hence, there is a need for more effective predictive techniques that can be used to measure and optimize energy usage of large arrays of connected Internet of Things (IoT) devices and control points that constitute modern built environments. In this paper, we propose a lightweight IoT framework for predicting energy usage at a localized level for optimal configuration of building-wide energy dissemination policies. Autoregressive Integrated Moving Average (ARIMA) as a statistical liner model could be used for this purpose; however, it is unable to model the dynamic nonlinear relationships in nonstationary fluctuating power consumption data. Therefore, we have developed an improved hybrid model based on the ARIMA, Support Vector Regression (SVRs) and Particle Swarm Optimization (PSO) to predict precision energy usage from supplied data. The proposed model is evaluated using power consumption data acquired from environmental actuator devices controlling a large functional space in a building. Results show that the proposed hybrid model out-performs other alternative techniques in forecasting power consumption. The approach is appropriate in building energy policy implementations due to its precise estimations of energy consumption and lightweight monitoring infrastructure which can lead to reducing the cost on energy consumption. Moreover, it provides an accurate tool to optimize the energy consumption strategies in wider built environments such as smart cities.

Improved multiobjective salp swarm optimization for virtual machine placement in cloud computing

In data center companies, cloud computing can host multiple types of heterogeneous virtual machines (VMs) and provide many features, including flexibility, security, support, and even better maintenance than traditional centers. However, some issues need to be considered, such as the optimization of energy usage, utilization of resources, reduction of time consumption, and optimization of virtual machine placement. Therefore, this paper proposes an alternative multiobjective optimization (MOP) approach that combines the salp swarm and sine-cosine algorithms (MOSSASCA) to determine a suitable solution for virtual machine placement (VMP). The objectives of the proposed MOSSASCA are to maximize mean time before a host shutdown (MTBHS), to reduce power consumption, and to minimize service level agreement violations (SLAVs). The proposed method improves the salp swarm and the sine-cosine algorithms using an MOP technique. The SCA works by using a local search approach to improve the performance of traditional SSA by avoiding trapping in a local optimal solution and by increasing convergence speed. To evaluate the quality of MOSSASCA, we perform a series of experiments using different numbers of VMs and physical machines. The results of MOSSASCA are compared with well-known methods, including the nondominated sorting genetic algorithm (NSGA-II), multiobjective particle swarm optimization (MOPSO), a multiobjective evolutionary algorithm with decomposition (MOEAD), and a multiobjective sine-cosine algorithm (MOSCA). The results reveal that MOSSASCA outperforms the compared methods in terms of solving MOP problems and achieving the three objectives. Compared with the other methods, MOSSASCA exhibits a better ability to reduce power consumption and SLAVs while increasing MTBHS. The main differences in terms of power consumption between the MOSCA, MOPSO, MOEAD, and NSGA-II and the MOSSASCA are 0.53, 1.31, 1.36, and 1.44, respectively. Additionally, the MOSSASCA has higher MTBHS value than MOSCA, MOPSO, MOEAD, and NSGA-II by 362.49, 274.70, 585.73 and 672.94, respectively, and the proposed method has lower SLAV values than MOPSO, MOEAD, and NSGA-II by 0.41, 0.28, and 1.27, respectively.

Modeling postharvest loss and water and energy use in Florida tomato operations

With a growing worldwide population, feeding 10 billion people by the year 2050 is the next global challenge. Fresh produce systems account for a significant fraction of total food and resource consumption due to their perishable nature, with postharvest quality being a key challenge. Production models for fresh produce are widely used and well adapted, whereas postharvest operations (PO) models have only recently been developed. The overarching goal is to quantify interactions of food quality, water and energy use in PO. In this study, an existing PO model was enhanced and implemented for a field grown tomato operation in Florida. Model estimates were compared with data from a representative operation, and were upscaled to obtain statewide estimates. The enhanced model was found to be the most sensitive to harvest frequency, quantity shipped to customer, and quantity harvested. At maximum grower profit, the model estimated water and energy quantities roughly 20% lower for each operation. The representative operation exceeded optimal water and energy usage because the farmers, despite having efficient production, commonly ”over-produce” far beyond optimal levels for reasons including risk of loss, tradition, low market prices, and large fixed costs of operation. Postharvest loss estimated by the model was 22% of quantity harvested for the representative operation. The upscaled regional postharvest losses were at 16% for the state of Florida. Operation-specific water and energy use from the case study were upscaled to give regional monthly estimates of 50.3 million liters and 28.3 million kWh, respectively. Such interactions provide insights into postharvest decisions made by commercial operations and impacts of these decisions on the food, water and energy system. The integrated modeling framework in this study can be extended to other crops and quantify interactions of water, energy and, postharvest losses to optimize efficient management practices.

Direct Digital Manufacturing in the Context of a Circular Economy

Additive manufacturing technologies have evolved from prototype to part production. Coupling this shift with organizational transformations evolved rapid prototyping industry into Direct Digital Manufacturing (DDM) industry. Any evolution in production systems is now, more than ever before, dependent on sustainability principles. The development of the DDM industry must be guided by these principles, taking the chance it offers to change the production paradigm. Circular Economy (CE) is a shift in the production and resource management model, and one of the ways to work towards sustainability. Coupling DDM with CE principles is thus contributing to change industrial production to a more sustainable one. This paper intends to relate DDM technologies with CE principles, to identify aspects where DDM is contributing to CE, as well as aspects that should be explored further for that purpose. It is observed that DDM can enable optimization of material and energy usage, modify logistics towards de-localized production and recycling and enable prolonged lifespan of products through better access to spare parts, for instance. It is also observed that DDM has good potential to shift materials usage towards natural materials, especially in a context of consumer or local community production, but not so much within the current industrial context. Education is shown to play a pivotal role, since incorporating circular economy principles in educational contexts should spark a shift in consumer perspectives, modifying demand and hence, industrial production.

Memory of Periodic Thermal Stimulation in an Immune Complex

AbstractProteins search their conformational space in order to attain the native fold and bind productively to relevant biological partners. Most proteins must be able to alternate between at least one active conformational state back to an inactive conformer, especially for macromolecules that perform work and need to optimize energy usage. This property may be invoked by a physical stimulus (eg. temperature) or by a chemical ligand, and may occur through mapping of the protein external environment onto a subset of protein conformers. We have stimulated with temperature cycles two partners of an immune complex before and after assembly. We have revealed with calorimetry that properties of the external stimulus are also found in the characteristics of the immune complex (i. e. periodic variations in the binding affinity). These results are important for delineating the bases of molecular memory ex vivo and may serve in the optimization of protein based sensors.

Optimization of Energy Usage of the Building Envelope Material at the Rental Office Buildings

This study aims to determine the effect of materials used in building envelopes on energy use in office buildings. To obtain energy use analysis data, ASHRAE 90.1 - 2013 based parameters are used in the Sefaira Building Simulation application. The building analyzed will be simulated, using variables such as sunlight conduction with roofs, walls, building window ratios, gap ratios in buildings, and use of electrical equipment per square meter. Based on the analysis carried out, the use of building energy by using standard material specifications from the application reaches 180 kWh / m2 / yr from what is supposed to be 79 kWh / m2 / yr. After being analyzed, a specification change is given to the liquid application so that the results of the analysis are 89 kWh / m2 / yr. So that the energy that can be reduced reaches 91 kWh / m2 / yr. This proves that the selection of building material specifications can affect energy use in rental office buildings.

Optimized energy consumption model for smart home using improved differential evolution algorithm

This paper proposes an improved enhanced differential evolution algorithm for implementing demand response between aggregator and consumer. The proposed algorithm utilizes a secondary population archive, which contains unfit solutions that are discarded by the primary archive of the earlier proposed enhanced differential evolution algorithm. The secondary archive initializes, mutates and recombines candidates in order to improve their fitness and then passes them back to the primary archive for possible selection. The capability of this proposed algorithm is confirmed by comparing its performance with three other well-performing evolutionary algorithms: enhanced differential evolution, multiobjective evolutionary algorithm based on dominance and decomposition, and non-dominated sorting genetic algorithm III. This is achieved by testing the algorithms' ability to optimize a multi-objective optimization problem representing a smart home with demand response aggregator. Shiftable and non-shiftable loads are considered for the smart home which model energy usage profile for a typical household in Johannesburg, South Africa. In this study, renewable sources include battery bank and rooftop photovoltaic panels. Simulation results show that the proposed algorithm is able to optimize energy usage by balancing load scheduling and contribution of renewable sources, while maximizing user comfort and minimizing peak-to-average ratio.

Nonstationary convective drying of raspberries, assisted by microwaves and ultrasound

This study was conducted to investigate the drying characteristics and quality of raspberries dried in non-stationary conditions. Seven drying programs were carried out, including convective (CV) drying as a reference. The non-stationary-hybrid programs were based on continuous CV drying with intermittent microwave and ultrasound assistance. The Midilli–Kucuk model was used successfully to describe the drying kinetics of raspberry fruits. The comparison between moisture ratio, drying rate, diffusion coefficient, energy consumption, and quality analysis, showed that non-stationary-hybrid drying improves process kinetics and results in less color change, higher anthocyanin retention, and better texture profile analysis characteristics with optimal energy usage than other methods. In addition, the physical and microstructural changes occurred during different drying processes were discussed.

Optimal variables estimation for energy reduction via a remote supervisory control: application to a counter-flow rotary dryer

This research anatomizes an automatic mode control of a counter flow rotary dryer by variable speed drive technology, to control moisture content of product in an organic fertilizer drying process. Effects of rotary dryer cyclone blower and biogas blower were investigated on a fertilizer production by the 3k factorial experiment technique. Response surface methodology was employed to determine optimum variables. The multiple-response was revealed that the rotary dryer, the cyclone blower, and the biogas blower were 90 %, 48 %, and 92 %, respectively. These values were observed to make the system to consume electricity at the rate of 8.91 kW/h and the product moisture was observed at 18.90 % db. To inspect control system accuracy and accountability, the results obtained from the mathematical model was compared with the data from supervisory control and data acquisition system and with the data from real measurement also. The output responses were the electric power used and the product moisture obtained. The statistical investigation determined the optimization of energy usage.

Digital Energy Hub for Optimizing Energy Usage: A Review

Energy usage as it relates to its security and management is an important aspect that all organizations having a vested stake in power production should be concerned with in these coming years. In this respect, harnessing the knowledge so that it may benefit various sectors, from the power producer right down to the customers, can be achieved by way of the digital energy hub, as proposed in this paper. We first put into context the workings and functions of such a digital platform, and then proceed to outline how it could benefit the many parties involved. Lastly, we take into account the challenges that needs to be addressed for its successful implementation, in which information security and privacy is a top priority amongst other issues.

Energy efficiency optimization in big data processing platform by improving resources utilization

Big data creates tremendous value for humanity, but also places a heavy burden on the environment. Energy consumption of computing platforms, especially big data processing platforms, cannot be ignored, and optimization of energy usage is imperative. To the out-of-core data processing, this paper proposes that the energy efficiency of a big data processing platform is closely related to the utilization of its computing resources; and an efficient resource allocation strategy for data processing tasks improves the platform’s resources utilization. To improve the resources utilization, different resources are allocated according to a task-related Best Resource Ratio (BRR for short), such as “cpu, disk, network in the ratio of 1:2:4″, rather than the resource’s quantity, such as “cpu = 1 GHz, network = 20MB/s”. We deduce the BRR of data processing tasks, and design a resource ratio based approach (R2), which includes a task scheduling algorithm and resource allocation algorithm, for energy efficiency optimization. Experiments show that the R2 approach can improve energy efficiency by 10%, 10% and 6% compared to FIFO, Capacity and Fair schedulers respectively, by maximizing resource utilization of a 12-node MapReduce platform.

Energy Audit with Innovative Technology Management for Resourcing the Clean Energy and Sustainability in India

Energy is an essential input to the overall development of the economy. It provides light and fuel to millions of households, industry, agriculture, commerce all service sectors and so on. Since energy is a critical component, development of conventional and non-conventional forms of energy for meeting the growing demand of the society at reasonable price and quality is the responsibility of the government. Energy Audit is an important and vital tool in the management of energy both at industry and nation level. It is required for improvising the efficient management practices in energy production, utilization and distribution with better technology and to thereby conserve for future growth and development. With the growing importance for industrial sectors in the country’s development and increasing energy consumptions, it has become essential and necessity to opt for more optimization methods in the consumption of energy. A saving of 15 to 30% can be done by optimal usage of energy at home. Energy conservation and safety is the topmost priority in today’s world to have a consistent growth, increase in production and to protect our environment. Energy audit and its management is instrumental to cope with the variation in the cost of energy, to identify the appropriate mixes of energy, to improvise the energy conservative equipment and technologies. Along with innovative technology for energy consumptions we also require innovative methods in the management of energy audit to help the country with efficient energy management system, reduce cost and save energy for future requirements. To study and understand the need for Energy audit with innovative technology management in India. To analyze its importance for resourcing clean energy and sustainability in India.

Simultaneous Energy and Water Optimisation in Shale Exploration

This work presents a mathematical model for the simultaneous optimisation of water and energy usage in hydraulic fracturing using a continuous time scheduling formulation. The recycling/reuse of fracturing water is achieved through the purification of flowback wastewater using thermally driven membrane distillation (MD). A detailed design model for this technology is incorporated within the water network superstructure in order to allow for the simultaneous optimisation of water, operation, capital cost, and energy used. The study also examines the feasibility of utilising the co-produced gas that is traditionally flared as a potential source of energy for MD. The application of the model results in a 22.42% reduction in freshwater consumption and 23.24% savings in the total cost of freshwater. The membrane thermal energy consumption is in the order of 244 × 103 kJ/m3 of water, which is found to be less than the range of thermal consumption values reported for membrane distillation in the literature. Although the obtained results are not generally applicable to all shale gas plays, the proposed framework and supporting models aid in understanding the potential impact of using scheduling and optimisation techniques to address flowback wastewater management.

Speed vs Efficiency and Storage Type in Portable Energy Systems

Portable power management systems must optimise power interfacing, storage and routing, to meet application specific functionality requirements. Two key aspects are reliability and efficiency. For reliable operation, it is required that powering on/off the system must occur in a planned manner. For efficient operation, it is desired that the system is powered for an optimal amount of time. maximizing its useful operational outcome per unit of energy consumed. This can be achieved by optimizing energy usage based on the anticipated energy income and power demand of duty-cycled power consumers. Both battery and supercapacitor storage can be employed to meet energy and power density demand, on both sides, and to enable fast transition from cold-starting to active power management. A simplified model is used to calculate the reliability of a simple solar-powered microsystem. The modelling of dynamically configurable interfacing and storage may enable a new generation of power management, providing reliable power from irregular and small energy sources.

Unconventional methods for pressure loss reduction in standard pipe elements

Abstract Reduction of pressure at pump houses are the most feasible and most advantageous as the pipe design method that is used, is more than 200 years old and is based up on 19th century production technology. My research focuses on a general approach on improving and reducing the pressure loss of these pipe elements with the help of non-conventional methods, thus resulting in a lower and more optimal energy usage of pump houses. The problematic zones are identified with the help of numerical modelling, geometry changes can be made and tested the same method. The geometrical changes aiming at pressure loss reduction follow non-conventional ideas, form hemodynamic and other biomechanics sources. Pipes in the pump houses are designed, for more than 50 years of operation. Even a small pressure loss reduction with this new method will mean large amount of energy saving in total. The results show that 15%–60% of pressure loss reduction is feasible, according to the complexity of the geometry. Pressure loss reduction will reduce energy consumption of water pumps which will result in a more efficient water works operation.