Search results

The purpose of this paper is to enhance control accuracy, energy efficiency and productivity of customized industrial robots by the proposed multi-objective trajectory optimization approach. To obtain accurate dynamic matching torques of the robot joints with optimal motion, an improved dynamic model built by a novel parameter identification method has been proposed.

This paper proposes a novel multi-objective optimal approach to minimize the time and energy consumption of robot trajectory. First, the authors develop a reliable dynamic parameters identification method to obtain joint torques for formulating the normalized energy optimization function and dynamic constraints. Then, optimal trajectory variables are solved by converting the objective function into relaxation constraints based on second-order cone programming and Runge–Kutta discrete method to reduce the solving complexity.

Extensive experiments via simulation and in real customized robots are conducted. The results of this paper illustrate that the accuracy of joint torque predicted by the proposed model increases by 28.79% to 79.05% over the simplified models used in existing optimization studies. Meanwhile, under the same solving efficiency, the proposed optimization trajectory consumes a shorter time and less energy compared with the existing optimization ones and the polynomial trajectory.

A novel time-energy consumption optimal trajectory planning method based on dynamic identification is proposed. Most existing optimization methods neglect the effect of dynamic model reliability on energy efficiency optimization. A novel parameter identification approach and a complete dynamic torque model are proposed. Experimental results of dynamic matching torques verify that the control accuracy of optimal robot motion can be significantly improved by the proposed model.

The purpose of this paper is to identify the most appropriate multidisciplinary data sources related with energy optimization decision support as well as the related methodologies, tools and techniques for data capturing and processing for each of them.

A review is conducted on the state-of-play of decision support systems for energy optimization, focussing on the municipal sector, followed by an identification of the most appropriate multidisciplinary data sources related with energy optimization decision support. An innovative methodology is outlined to integrate semantically modeled data from multiple sources, to assist city authorities in energy management.

City authorities need to lead relevant actions toward energy-efficient neighborhoods. Although there are more and more energy and other related data available at the city level, there are no established methods and tools integrating and analyzing them in a smart way, with the purpose to support the decision-making process on energy use optimization.

A novel multidimensional approach is proposed, using semantic technologies to integrate data from multiple sources, to assist city authorities to produce short-term energy plans in an integrated, transparent and comprehensive way.

The purpose of this paper is to develop an efficient termination control strategy of air-vented dryer in term of energy saving, improving smoothness and reducing microscopic damage of fiber.

A simple, low cost termination control strategy is developed by testing the instantaneous humidity of exhaust air and then deducing the drying degree of fabric in process. The practicability evaluation of this novel strategy was investigated by using both experimental and mathematical approaches. The effect of termination control strategy on drying efficiency and fabric apparent properties were also discussed.

Termination control strategy significantly affects drying time, energy consumption, smoothness and microscopic of fiber. Specially, a novel termination control strategy that the combination of equilibrium moisture content of fabric in ambient environment and relative humidity of exhaust air in exhaust duct is workable and can save 25.2 percent of energy consumption, 26.7 percent of the drying time and improve 0.7 grade of the appearance smoothness, as well as significantly reduce the microscopic damage of fiber compare to the original control strategy of dryer. This indicates possible ways to minimize drying energy consumption and dryer damage by reducing unnecessary migrate out of the water from the clothes.

The paper is helpful in not only the development of new drying product but also the optimization of appearance smoothness of fabric after drying and reduce the microscopic damage of fiber.

A novel termination control strategy of dryer is applied to improve drying efficiency of dryer and reduce fabric damage.

This study aims to compare the performance of two nature-inspired metaheuristics inside Grasshopper in optimizing daylighting and energy performance against brute force in terms of the resemblance to ideal solution and calculation time.

The simulation-based optimization process was controlled using two population-based metaheuristic algorithms, namely, the genetic algorithm (GA) and particle swarm optimization (PSO). The objectives of the optimization routine were optimizing daylighting and energy consumption of a standard reference office while varying the urban context configuration in Alexandria, Egypt.

The results from the GA and PSO were compared to those from brute force. The GA and PSO demonstrated much faster performance to converge to design solution after conducting only 25 and 43% of the required simulation runs, respectively. Also, the average proportion of the resulted weighted sum optimization (WSO) per case using the GA and PSO to that from brute force algorithm was 85 and 95%, respectively.

The work of this paper goes beyond the current practices for showing that the performance of the optimization algorithm can differ by changing the urban context configuration while solving the same problem under the same design variables and objectives.

This paper aims to provide prolonging network lifetime and optimizing energy consumption in mobile wireless sensor networks (MWSNs). Forming clusters of mobile nodes is a great task owing to their dynamic nature. Such clustering has to be performed with a higher consumption of energy. Perhaps sensor nodes might be supplied with batteries that cannot be recharged or replaced while in the field of operation. One optimistic approach to handle the issue of energy consumption is an efficient way of cluster organization using the particle swarm optimization (PSO) technique.

In this paper two improved versions of centralized PSO, namely, unequal clustering PSO (UC-PSO) and hybrid K-means clustering PSO (KC-PSO), are proposed, with a focus of achieving various aspects of clustering parameters such as energy consumption, network lifetime and packet delivery ratio to achieve energy-efficient and reliable communication in MWSNs.

Theoretical analysis and simulation results show that improved PSO algorithms provide a balanced energy consumption among the cluster heads and increase the network lifetime effectively.

In this work, each sensor node transmits and receives packets at same energy level only. In this work, focus was on centralized clustering only.

To validate the proposed swarm optimization algorithm, a simulation-based performance analysis has been carried out using NS-2. In each scenario, a given number of sensors are randomly deployed and performed in a monitored area. In this work, simulations were carried out in a 100 × 100 m² network consisting 200 nodes by using a network simulator under various parameters. The coordinate of base station is assumed to be 50 × 175. The energy consumption due to communication is calculated using the first-order radio model. It is considered that all nodes have batteries with initial energy of 2 J, and the sensing range is fixed at 20 m. The transmission range of each node is up to 25 m and node mobility is set to 10 m/s.

This proposed work utilizes the swarm behaviors and targets the improvement of mobile nodes’ lifetime and energy consumption.

PSO algorithms have been implemented for dynamic sensor nodes, which optimize the clustering and CH selection in MWSNs. A new fitness function is evaluated to improve the network lifetime, energy consumption, cluster formation, packet transmissions and cluster head selection.

Assembly lines are one of the places where energy consumption is intensive in manufacturing enterprises. The use of robots in assembly lines not only increases productivity but also increases energy consumption and carbon emissions. The purpose of this paper is to minimize the cycle time and total energy consumption simultaneously in parallel robotic assembly lines (PRAL).

Due to the NP-hardness of the problem, A Pareto hybrid discrete firefly algorithm based on probability attraction (PHDFA-PA) is developed. The algorithm parameters are optimized using the Taguchi method. To evaluate the results of the algorithm, a multi-objective programming model and a restarted simulated annealing (RSA) algorithm are used.

According to the comparative study, the PHDFA-PA has a competitive performance with the RSA. Thus, it is possible to achieve a sustainable PRAL through the proposed method by addressing the cycle time and total energy consumption simultaneously.

To the best knowledge of the authors, this is the first study addressing energy consumption in PRAL. The proposed method for PRAL is efficient in solving the multi-objective balancing problem.

The purpose of the paper is to analyze the development needs and opportunities in the distribution system operators’ (DSO) tariff structures in the smart-grid environment.

The impacts of the distribution pricing schemes for the stakeholders and their requirements for the tariff structures are evaluated by qualitative analyses. Furthermore, there is a case analysis concerning the practical development possibilities of the DSO tariff structures in Finland.

Major finding of the paper is that the demand-based power band tariff is the optimal solution for the DSO pricing structure, when taking into account the real-life limitations and the requirements of the stakeholders.

Outcomes of the paper can be applied in practice in design of the pricing schemes in the electricity distribution. Incentive provision impacts and cost reflectivity of the DSO tariffs can be improved by implementing the suggested pricing structure.

The paper provides a novel viewpoint on the study of the DSO tariff design by considering the impacts of the pricing for the stakeholders and their requirements for tariff structure. Furthermore, the real-life limitations in the tariff design have been taken into account by analyzing the development options in Finland. Results are useful, especially for the DSOs, regulators and academics, who are working with the tariff development issues.

In modern cloud services, resource provisioning and allocation are significant for assigning the available resources in efficient way. Resource management in cloud becomes challenging due to high energy consumption at data center (DC), virtual machine (VM) migration, high operational cost and overhead on DC.

In this paper, the authors proposed software-defined networking (SDN)-enabled cloud for resource management to reduce energy consumption in DC. SDN-cloud comprises four phases: (1) user authentication, (2) service-level agreement (SLA) constraints, (3) cloud interceder and (4) SDN-controller.

Resource management is significant for reducing power consumption in CDs that is based on scheduling, VM placement, with Quality of Service (QoS) requirements.

The main goal is to utilize the resources energy effectively for reducing power consumption in cloud environment. This method effectively increases the user service rate and reduces the unnecessary migration process.

As a result, the authors show a significant reduction in energy consumption by 20 KWh as well as over 60% power consumption in the presence of 500 VMs. In future, the authors have planned to concentrate the issues on resource failure and also SLA violation rate with respect to number of resources will be decreased.

This paper aims to identify all variables and parameters related to business and emission within the petrochemical industry. The variables and parameters specified will be modeled into a system dynamic model that will be a baseline for the proposed best scenario(s) to address the business issue related to emission reduction in the petrochemical industry.

Literature review and stakeholder interviews were conducted to define the key factors contributing to the emission reduction of the petrochemical industry. The key factors are then developed into a system dynamic model to measure the quantitative impact of changes in those variables on emission and industry profitability.

This paper provides an analysis of system dynamic model. It suggests that process optimization can lead to a slight amount reduction in emissions. In contrast, a significant reduction shows in the simulation result of bio-based feedstock utilization and implementation of advanced technology. To sustain the emission reduction, strong commitment from stakeholders and support from the government will play an important role.

This research is limited to problem analysis of the primary product (high-value chemical) of the petrochemical industry by only considering the changes in the key factors of emission reduction.

This paper includes implications for interventions that can be imposed to reduce emission while retaining the business profitability.

The contribution of this study is to find the best scenario that can boost emission reduction within Indonesia’s petrochemical industry.

Fishes are important sources of good and high-quality protein in developing countries. Spoilage and keeping quality of fish especially in the tropics is temperature dependence as high temperature and relative humidity accelerate the process of spoilage and fish keeping quality. Fish dehydration removed moisture and extended the shelf life of dried fish. Drying involves removal of moisture from fish as a result of heat and mass transfer done under controlled conditions. This study delves into various drying techniques and drying kinetics of fish.

The review examines fish drying kinetics and the various drying models applicable to fish drying.

This review showed that moisture content and colour of dried fish are affected by time and power level. It was also found that the moisture content of the dried fish varied according to the drying method used. Also, as drying power and drying rate varied inversely with drying time. Eight different thin layer drying models were examined for evaluation of drying data for all the experimental conditions involving fish drying. It was found that the quality of the dried fish decreased with drying. Higher values of effective moisture diffusivity have been found to increase moisture velocity within fish samples which improve removal of moisture to reach equilibrium moisture content at specified relative humidity. However, based on this, effective moisture diffusivity could be a useful parameter to design an effective drying method in terms of time, energy consumption and cost to prolong the storage life of dried fish samples. Drying kinetics and different drying models were considered and explained. The use of these models was considered to be important in choosing appropriate drying conditions for effective drying and to get good quality dried fish samples.

The review considers few available literatures on the subject matter.

The review explores the possibility of creating more awareness for more in-depth research on fish drying kinetics and their usefulness in fish preservation.

This outcome of this study is important to researchers, policymakers and regulatory agencies in developing countries on fish preservation.