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In this paper, a detailed dryer section audit was conducted on a paper machine to evaluate its energy performance. The methodology and instruments used for field measurements are introduced as an important part of dryer audit. The deficiency points which limit drying performance of the paper machine are identified based on the tested data, It was found that significant energy savings can be achieved in the studied paper machine. [ABSTRACT FROM AUTHOR]

Evolutionary Algorithms (EAs) are routinely applied to solve a large set of optimization problems. Traditionally, their performance in solving those problems is analyzed using the fitness quality and computing time, and the effect of evolutionary operators on both metrics is routinely used to compare different versions of EAs. Nevertheless, scientists face nowadays the challenge of considering the energy efficiency in addition to computational time, which requires studying the energy consumption of algorithms. This paper discusses the interest of introducing power consumption as a new metric to analyze the performance of standard genetic programming (GP). Two well-studied benchmark problems are addressed on three different computing platforms, and two different approaches to measure the power consumption have been tested. Analyzing the population size, the results demonstrates its influence on the energy consumed: a non-linear relationship was found between size and energy required to complete an experiment. This analysis was extended to the cache memory and results show an exponential growth in the number of cache misses as the population size increases, which affects the energy consumed. This study shows that not only computing time or solution quality must be analyzed, but also the energy required to find a solution. Summarizing, this paper shows that when GP is applied, specific considerations on how to select parameter values must be taken into account if the goal is to obtain solutions while searching for energy efficiency. Although the study has been performed using GP, we foresee that it could be similarly extended to EAs. [ABSTRACT FROM AUTHOR]

Robot swarm networking systems (RSNS) exhibit complex emergent behavior by using local control laws based on spatial information from nearby environment and adjacent robot agents. The consensus behavior of the RSNS depends on a set of parameters of robot agent algorithms or system parameters for their operation, issued mainly by the operator. The challenge in the RSNS is developing techniques for the operator to interact with the RSNS in order to make system behavior adaptive to changes in system configuration and for operator commands without having to handle them individually. Another challenge is saving energy consumption over the robot agents in the RSNS by reducing the number of information exchange between robot agents when the system configuration is spread out the network from the operator. To address these issues, this paper presents an energy-efficient control approach for system configuration propagation with self-triggering control. The proposed method controls the RSNS operation by indirectly propagating the system configuration within the framework of local rules. Moreover, a self-triggered propagation model is designed according to the convergence rate of configuration propagation in order to save and to balance energy consumption among robot agents in the RSNS. This model is then extended to an optimal timing control, where the operator determines its next input time without having to keep track of all the states of robot agents. Theoretical analysis and simulation results are performed to demonstrate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

This paper studies the green single-machine scheduling problem that considers the delay cost and the energy consumption of manufacturing equipment and builds its integrated optimization model. The improved ant colony scheduling algorithm based on the Pareto solution set is used to solve this problem. By setting the heuristic information, state transition rules, and other core parameters reasonably, the performance of the algorithm is improved effectively. Finally, the model and the improved algorithm are verified by the simulation experiment of 10 benchmark cases. [ABSTRACT FROM AUTHOR]

Wireless sensor network is a collection of sensor nodes designed with different routing capabilities to operate on real-world applications. In extreme environments, real-time applications of wireless sensor network ensure exchange of data, a difficult one between the sensor nodes, when less resources are consumed. Therefore, researchers are developing a routing protocol including optimal routing procedures to increase the longevity of the networks. In this paper, an improved route extension architecture is developed in wireless sensor network environment. This Improved Greedy Perimeter Stateless Routing is proposed to offer an improved transmission coverage and reduced power consumption capability. It deploys a periodic broadcast of hello message (or control messages) including the positional information between the sensor nodes. The experimental result concludes that the Improved Greedy Perimeter Stateless Routing method achieves improved routing capabilities than the traditional hybrid protocols like LEACH and Greedy Perimeter Stateless Routing. [ABSTRACT FROM AUTHOR]

Gas fractionation unit is one of the main processes in refinery industries and consumes a significant portion of energy. Optimal operation of gas fractionation unit could reduce energy consumption and therefore minimize the production cost. This paper reports a real application of multi-objective optimization in a gas fractionation unit, in which energy consumption is minimized and product yield is maximized. The optimization algorithm is based on the non-dominate sorting genetic algorithm and the sequential unconstrained minimization technique. The optimization results show that the yield of product is increased and the energy consumption is reduced in the gas fractionation unit. [ABSTRACT FROM PUBLISHER]

The presence of outliers is the main reason leading to ineffectiveness of advanced data-driven control methods in electric arc furnace systems. This paper proposes a hybrid method dedicated to detecting outliers in electric arc furnace systems, where process data are characterized as unlabeled, imbalanced, non-stationary and noisy. First, the raw data are divided into certain number of clusters. Then, with each cluster, a one-class classifier can be trained. So with these well-trained sub-models, new test points can be investigated. Those points that are rejected by all sub-models will be labeled as outliers. With the combination of one-class classification and clustering technique, the intricate data in electric arc furnace can be processed effectively. In addition, the detector will be updated with a specific strategy to enhance its adaptiveness. A series of experiments are carried out, and comparative results have shown the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Wireless sensor network is a collection of sensor nodes designed with different routing capabilities to operate on real-world applications. In extreme environments, real-time applications of wireless sensor network ensure exchange of data, a difficult one between the sensor nodes, when less resources are consumed. Therefore, researchers are developing a routing protocol including optimal routing procedures to increase the longevity of the networks. In this paper, an improved route extension architecture is developed in wireless sensor network environment. This Improved Greedy Perimeter Stateless Routing is proposed to offer an improved transmission coverage and reduced power consumption capability. It deploys a periodic broadcast of hello message (or control messages) including the positional information between the sensor nodes. The experimental result concludes that the Improved Greedy Perimeter Stateless Routing method achieves improved routing capabilities than the traditional hybrid protocols like LEACH and Greedy Perimeter Stateless Routing. [ABSTRACT FROM AUTHOR]

At present, open-loop synchronous control for hydraulic systems widely using synchronous valves has low synchronous accuracy due to weak anti-bias capacity, and is hard to adjust synchronization velocity, so it could not meet the requirements of accurate synchronization under severe conditions. In this paper, a load-sensing synchronous control is developed to obtain accurate synchronization in open-loop control, which is made up of a load-sensing unit and a synchronous valve. In the load-sensing loop, a load-sensing pump supplies pressure and flow required by the system through pressure closed-loop control, and a load-sensing valve could improve the capability of anti-offset loads by pressure compensation. The synchronous valve is between the load-sensing pump and load-sensing valve, to achieve equal distribution of flow supplied by the pump, which could improve the divider accuracy of the load-sensing system. A test system within load-sensing synchronous control is established, and then comparison experiments under different partial loads are carried out. The experimental results show that, compared with the traditional synchronous valve control, the load-sensing synchronous control has the advantages of higher synchronous precision, higher energy efficiency and also the ability of velocity regulation. Load-sensing synchronous control has the potential of high precision synchronization control in server environments. [ABSTRACT FROM AUTHOR]

This paper presents a radial basis function prediction model improved by differential evolution algorithm for coking energy consumption process, which is very difficult to get online and real time because of the complex process. In the energy consumption prediction model, target flue temperature, flue suction, water content, volatile coal and coking time are considered as input variables, and coking energy consumption as output variables. To overcome the shortcomings of radial basis function network, such as poor learning ability and slow convergence speed, the energy consumption prediction model optimized by differential evolution algorithm is improved. Using the strong global search ability of differential evolution algorithm, the center value, width and output weight of the basis function in radial basis function network is obtained by differential evolution algorithm. Then the optimal values are taken as the center value, width and output weight of the of radial basis function neural network. The results show that the improved radial basis function prediction has higher accuracy, stability and training speed of the network. The radial basis function prediction model has great significance in reducing coking energy consumption, saving enterprise costs, increasing coke production and improving enterprise economic benefits. [ABSTRACT FROM AUTHOR]

In order to reduce the energy consumption of the welding robot and ensure the cooperative movement of the robot joints, a trajectory planning method with optimal energy consumption based on improved sparrow search algorithm is proposed. Firstly, the trajectory planning model with optimal energy consumption is established based on the joint torque and angular velocity of the robot. To make the velocity, acceleration and jerk of each joint of the robot be bounded and continuous, the joint space trajectory is constructed with seventh degree B-spline curve. The total energy consumption of the robot is calculated by combining kinematic and dynamic parameters. On the basis of improved sparrow search algorithm, the time series corresponding to the optimal energy consumption is solved by using elite reverse learning, non-dominated sorting and Gaussian-Cauchy variation strategy, and then the optimal continuous motion trajectory of energy consumption is planned. The simulation results show that the proposed method can not only achieve continuous smooth control objective, but also effectively reduce energy consumption. [ABSTRACT FROM AUTHOR]

Background: In this work, an accurate low-cost power microwatt-meter is designed, built, and tested. This microwatt-meter is found to be capable of measuring power consumption in a wide range of devices and sensors, such as nodes in wireless sensor networks, microcontrollers in Internet of Things devices, and Field Programmable Gate Arrays. Methods: The paper examines the usage of the microwatt-meter for the measurement of power consumption in digital devices as they execute program instructions, ranging from a low-level single step (instruction) in an assembly program to a high-level sophisticated program or system. Results: The results obtained showed that the microwatt-meter is highly accurate. It can measure the energy consumed by any of the devices/programs/instructions in a digital system with accuracy levels ranging between 0.5% and 2.5% (depending on the application). Conclusion: The accuracy of the microwatt-meter is validated using the fundamental conservation of energy law and a comparison with multimeter current measurements is presented. [ABSTRACT FROM AUTHOR]

A novel performance seeking control) method based on Beetle Antennae Search algorithm is proposed to improve the real-time performance of performance seeking control. The Beetle Antennae Search imitates the function of antennae of beetle. The Beetle Antennae Search has better real-time performance because of the objective function only calculated twice in Beetle Antennae Search at each iteration. Moreover, the Beetle Antennae Search has global search ability. The performance seeking control simulations based on Beetle Antennae Search, Genetic Algorithm and particle swarm optimization are carried out. The simulations show that the Beetle Antennae Search has much better real-time performance than the conventional probability-based algorithms Genetic Algorithm and particle swarm optimization. The simulations also show that these three probability-based algorithms can get better engine performance, such as more thrust, less specific fuel consumption and less turbine inlet temperature. [ABSTRACT FROM AUTHOR]

Power allocation plays an important and challenging role in fuel cell and supercapacitor hybrid electric vehicle because it influences the fuel economy significantly. We present a novel Q-learning strategy with deterministic rule for real-time hybrid electric vehicle energy management between the fuel cell and the supercapacitor. The Q-learning controller (agent) observes the state of charge of the supercapacitor, provides the energy split coefficient satisfying the power demand, and obtains the corresponding rewards of these actions. By processing the accumulated experience, the agent learns an optimal energy control policy by iterative learning and maintains the best Q-table with minimal fuel consumption. To enhance the adaptability to different driving cycles, the deterministic rule is utilized as a complement to the control policy so that the hybrid electric vehicle can achieve better real-time power allocation. Simulation experiments have been carried out using MATLAB and Advanced Vehicle Simulator, and the results prove that the proposed method minimizes the fuel consumption while ensuring less and current fluctuations of the fuel cell. [ABSTRACT FROM AUTHOR]

Inconel-800 super alloy is a newly difficult-to-cut material. To improve the cutting conditions for this metal, sustainable methods in which minimum quantity lubrication enhanced with suspended nanoparticle were employed. This work also aims to model the relationship between process parameters (cutting speed, feed per tooth, depth of cut, and corner radius of cutting tool) and machining responses (surface roughness, specific cutting energy, cutting power, and material removal rate) using orthogonal array design of experiment and response surface methodology. Non-dominated sorting genetic algorithm was used to solve the multi-objective optimization problems in terms of energy, productivity, and quality of the machining process. The results indicate that the application of the response surface methodology model in combination with non-dominated sorting genetic algorithm is appropriate for this study due to the goodness of fit of response surface methodology and the global optimum solution of genetic algorithm. Because multi-objective optimization gives multiple solutions, Pareto plot and data mining are employed to support the selection of process parameters that can save time and cost and increase energy efficiency, meanwhile, simultaneously improve productivity and surface quality. The research results show that the specific cutting energy and energy consumption can be reduced up to 20.2% and 6.4%, respectively. [ABSTRACT FROM AUTHOR]

The flexible job shop problem (FJSP), as one branch of the job shop scheduling, has been studied during recent years. However, several realistic constraints including the transportation time between machines and energy consumptions are generally ignored. To fill this gap, this study investigated a FJSP considering energy consumption and transportation time constraints. A sequence-based mixed integer linear programming (MILP) model based on the problem is established, and the weighted sum of maximum completion time and energy consumption is optimized. Then, we present a combinational meta-heuristic algorithm based on a simulated annealing (SA) algorithm and an artificial immune algorithm (AIA) for this problem. In the proposed algorithm, the AIA with an information entropy strategy is utilized for global optimization. In addition, the SA algorithm is embedded to enhance the local search abilities. Eventually, the Taguchi method is used to evaluate various parameters. Computational comparison with the other meta-heuristic algorithms shows that the improved artificial immune algorithm (IAIA) is more efficient for solving FJSP with different problem scales. [ABSTRACT FROM AUTHOR]

Power allocation plays an important and challenging role in fuel cell and supercapacitor hybrid electric vehicle because it influences the fuel economy significantly. We present a novel Q-learning strategy with deterministic rule for real-time hybrid electric vehicle energy management between the fuel cell and the supercapacitor. The Q-learning controller (agent) observes the state of charge of the supercapacitor, provides the energy split coefficient satisfying the power demand, and obtains the corresponding rewards of these actions. By processing the accumulated experience, the agent learns an optimal energy control policy by iterative learning and maintains the best Q-table with minimal fuel consumption. To enhance the adaptability to different driving cycles, the deterministic rule is utilized as a complement to the control policy so that the hybrid electric vehicle can achieve better real-time power allocation. Simulation experiments have been carried out using MATLAB and Advanced Vehicle Simulator, and the results prove that the proposed method minimizes the fuel consumption while ensuring less and current fluctuations of the fuel cell. [ABSTRACT FROM AUTHOR]

A novel performance seeking control) method based on Beetle Antennae Search algorithm is proposed to improve the real-time performance of performance seeking control. The Beetle Antennae Search imitates the function of antennae of beetle. The Beetle Antennae Search has better real-time performance because of the objective function only calculated twice in Beetle Antennae Search at each iteration. Moreover, the Beetle Antennae Search has global search ability. The performance seeking control simulations based on Beetle Antennae Search, Genetic Algorithm and particle swarm optimization are carried out. The simulations show that the Beetle Antennae Search has much better real-time performance than the conventional probability-based algorithms Genetic Algorithm and particle swarm optimization. The simulations also show that these three probability-based algorithms can get better engine performance, such as more thrust, less specific fuel consumption and less turbine inlet temperature. [ABSTRACT FROM AUTHOR]

Solar energy exploitation through photovoltaic technology has demonstrated a sustainable way for curbing energy needs and cutting environmental issues happening due to emissions of carbon dioxide, CO2 from the usage of non-renewable energy resources. The purpose of this study was to reduce metallization by choosing optimal metallization on silicon photovoltaic wafer. Using numerical study, the effects of the number of busbars, fingers, and soldering/probe points were analyzed and also the study of the size of busbar and finger was carried out to find the optimal value for each which assures better performance. It is revealed that increasing the number and size of busbars, fingers, and probe points result in increasing fill factor, however, the efficiency of the device is limited to a number which provides the best optimal performance in terms of efficiency, whereas increasing the size (width) of the parameter result in a decrease in efficiency increasing shading factor. The optimal value of prescribed parameters was recorded as 4, 82, and 20 numbers of busbars, fingers, and probe points, respectively, while an optimal value of the width of busbar and finger is found as 0.5 mm and 60 µm, respectively. These values attained efficiency and fill factor above 20% and 80%, respectively. This study finds a realistic method to further diminish the metallization, improve the performance, and reduce the cost of often used industrial silicon photovoltaic cells. [ABSTRACT FROM AUTHOR]

Information regarding the annual meeting of the United Kingdom Automatic Control Council (UKACC) hosted by the Institution of Engineering and Technology (IET) held at the Savoy Place in London, England on October 17, 2012 is presented. Topics include motion control, estimation theory, and energy efficiency. The meeting featured several academics and researchers including Keith Burnham, Chris Edwards, and Andrew Plummer.