Motion Planning of an Inchworm Robot Based on Improved Adaptive PSO

Comparison of energy consumption and specific energy requirements of different methods for drying mushroom slices

Petroleum is an indispensable and important fuel for the development of modern industry, as well as an indispensable basic source of organic chemical raw materials and transportation fuels. However, the composition of petroleum is extremely complex and can only be fully utilized after proper separation. According to the different processes, the oil processing technology can be divided into primary processing and secondary processing. Among them, secondary processing refers to other processing processes using crude oil distillation products as raw materials, including catalytic cracking (FCC) and hydrocracking (HC). However, in today’s society, businesses face major challenges due to issues such as pollution and energy crisis. For example, low energy consumption and excessive pollutant emissions have resulted in a great waste of resources, while high environmental policy costs have caused many economic burdens. Minimizing costs and improving economic benefits have become the goals of modern enterprise management. At the same time, particle swarm optimization, as a calculation method with strong global search ability and fast convergence speed, can provide a new way of thinking for model optimization, and has been widely used in practical production and life. In this paper, the experimental analysis method and data analysis method are used to better understand the results of multi-objective optimization of FCC separation system through experiments. According to the experimental results, the recirculation flow rates in the main fractionation tower were 23100, 38710, 34900, and 42410 kg/h, respectively. It can be seen that the energy consumption is effectively reduced and the yield is improved. The above results can provide an important reference for the design and optimization of the FCC separation system.

On the premise of ensuring route safety, an intelligent route planning algorithm based on multi-strategy particle swarm optimization is proposed with the lowest energy consumption as the goal. The energy consumption model is established based on airline meteorological information, and a multi-objective fitness function is constructed with energy consumption, airline smoothness and collision penalty as optimization objectives. An adaptive mean velocity updating strategy is proposed, and nonlinear inertia weight is introduced, and Tent chaotic mapping is adopted to dynamically adjust the development ability and exploration ability of the algorithm to avoid falling into local optimal solution. The strategy of updating the position of the worst particle is put forward, and the particles with the worst fitness are updated by coordinate cross, which improves the overall particle quality of the population and speeds up the efficiency of population optimization. The simulation results show that the improved multi-strategy particle swarm optimization algorithm is superior to PSO algorithm in optimization ability and convergence speed.

ABSTRACTNonlinearity in parallel compliance can be exploited to improve the performance of locomotion systems in terms of (1) energy efficiency, (2) control robustness, and (3) gait optimality; that is, attaining energy efficiency across a set of motions. Thus far, the literature has investigated and validated only the first two benefits. In this study, we present a new mathematical framework for designing nonlinear compliances in cyclic tasks encompassing all three benefits. We present an optimization‐based formulation for each benefit to obtain the desired compliance profile. Furthermore, we analytically prove that, compared to linear compliance, using nonlinear compliance leads to (1) lower energy consumption, (2) better closed‐loop performance, specifically in terms of tracking error, and (3) a higher diversity of natural frequencies. To compare the performance of linear and nonlinear compliance, we apply the proposed methods to a diverse set of robotic systems performing cyclic tasks, including a 2‐DOF manipulator, a 3‐DOF bipedal walker, and a hopper model. Compared to linear compliance, the nonlinear compliance leads to better performance in all aspects; for example, a 70% reduction in energy consumption and tracking error for the manipulator simulation. Regarding gait optimality, for all robotic simulation models, compared to linear compliance, the nonlinear compliance has lower energy consumption and tracking error over the considered set of motions. The proposed analytical studies and simulation results strongly support the idea that using nonlinear compliance significantly improves robotic system performance in terms of energy efficiency, control robustness, and gait optimality.

With climate change concerns on the rise, finding efficient and sustainable ways to separate carbon dioxide from industrial gas mixtures has become increasingly important. Membrane‐based gas separation technologies offer a promising solution due to their low energy consumption, low emissions, and ease of operation. In this study, we dug deep into theoretical energy consumption and practical optimization strategies for CO2 separation using such technologies. Our results showed that CO2 separation can be achieved with relatively low specific energy consumption, ranging from 0.09 to 0.27 MJe/kg CO2, depending on feed CO2 concentration. By conducting process simulations, we determined the optimal feed gas pressure and membrane surface area required to achieve a CO2 absorption ratio of 90% (mol). We also analyzed the effects of CO2 permeation and selectivity on energy consumption and membrane area, revealing that increasing both can significantly reduce energy consumption, albeit with more membrane surface area required. Using seepage flow circulation was found to be a particularly effective way to improve feed CO2 concentration and reduce energy consumption. To optimize and improve the capturing process, we conducted exergy analysis in each of the five stages of optimization, reporting Cooling Utilities (MW), Heating Utilities (MW), and Total Utilities (MW) for each step. Our results showed that in the optimal mode, Total Utilities (MW) were reported as 228.1, highlighting the potential of membrane‐based CO2 separation for carbon capture and storage applications. This study provides valuable insights and practical strategies for achieving efficient and sustainable CO2 separation.

Present complementary metal–oxide–semiconductor (CMOS) technology with scaled channel lengths exhibits higher energy consumption in designing secure electronic circuits against hardware vulnerabilities and breaches. Specifically, CMOS sense amplifier-based secure differential power analysis (DPA) countermeasures at scaled channel lengths show large energy consumption, with increased vulnerability. Additionally, spin-transfer torque magnetic tunnel junction (STT-MTJ) and CMOS-based logic-in-memory (LiM) cells demonstrate high energy consumption due to the large write current requirement of the STT-MTJ and poor MOS device performance at scaled channel lengths. This paper for the first time leverages emerging tunnel field effect transistor (TFET) steep-slope device characteristics and compatible non-volatile STT-MTJ devices for enhanced hardware security with ultra-low energy consumption at lower supply voltages. TFET-based sense amplifier-based logic (SABL) gates are proposed that achieve 3× lower energy consumption than the Si FinFET SABL designs. Further, utilizing TFET SABL gates, a TFET PRIDE S-box is designed that exhibits higher DPA resilience with 3.2× lower energy consumption than the FinFET designs. With the resulting lower static power consumption, TFET SABL-based cryptosystems are thus less vulnerable to static power side-channel attacks. Additionally, the proposed STT-MTJ and TFET LiM gates achieve 4× lower energy consumption than the STT-MTJ and FinFET designs. Lastly, these gates are explored in a logic encryption/locking technique that shows 3.1× lower energy consumption than the STT-MTJ and FinFET-based design.

The purpose of this study is to conduct data mining research on the time series energy consumption dataset of primary school buildings. Researches done so far mainly focused on cluster analysis and association analysis. In this article, python language is used as the carrier; Κ-shape algorithm and apriori algorithm are adapted to perform cluster analysis and association analysis on the time series energy consumption data of primary school buildings, from the perspective of the subentry and total energy consumption. The final result shows that the energy consumption curve and association rules obtained through clustering and association can effectively reflect the operating characteristics of primary school buildings. In terms of clustering, regardless of the subentry energy consumption or the total energy consumption, there are different characteristics of energy consumption patterns in winter and summer, which depend on the school schedule. With respect to association rules, there are different chain relationships between itemized total energy consumptions in the primary school building. In particular, heating natural gas consumption and cooling consumption mainly determine the changes in other energy consumption.

Demand-driven optimization method for thruster configuration of underwater vehicles considering capability boundary and system efficiency

Electrochemical oxidation of COD from real textile wastewaters: Kinetic study and energy consumption

BMUDF: Hybrid Bio-inspired Model for fault-aware UAV routing using Destination-aware Fan shaped clustering

Effect of high-pressure grinding rolls pretreatment on the preparation of vanadium-titanium magnetite pellets

The GLOSA (Green Light Optimal Speed Advisory) system provides speed advice to drivers so that drivers can pass through congested intersections at right instant with shorter time and lower energy consumption. Traditional GLOSA system only considers the SPaT (Signal Phase and Timing) of traffic light. However, two another important factors, namely queuing effect and actual tracking error of drivers, are seldomly considered, which degrades the actual performance of the GLOSA system. Intelligent connected vehicles based on V2I (Vehicle to Infrastructure) have great application potential in solving this problem. In this study, firstly, a vehicle queue length estimation method based on V2I technology is proposed to predict the effective green light time. Secondly, a hierarchical GLOSA system is developed, where the upper layer provides the global recommended optimal speed aiming at minimizing energy consumption, while the bottom layer provides the modified recommended speed considering the driver's tracking error. Finally, the tracking error of the driver when executing the recommended speed is derived based on the real-world experiment. Corresponding simulation and field test platforms are also established. Results show that compared with the traditional GLOSA system, the improved GLOSA system considering the vehicle queuing effect and driving error can effectively improve the energy-saving performance of the vehicle.

Many mission critical devices are increasing with upcoming 5G network to fulfill a low latency for a real time network service on smart factory, autonomous vehicle, etc. Distributed cloud computing system also has a key role to execute the various mobile devices, because, an edge computing is the nearest from the mobile devices to provide low latency and computation energy consumption. In this paper, we consider the autonomous vehicles with video live streaming services. Especially, the vehicles require a low transmission delay as within 10 ms. To reduce a latency with low energy consumption, we propose a service chaining offloading decision with a deep reinforcement learning. We split tasks of the vehicle per service function blocks which have their own role. So it can do partial offloading and user association in a On-Device Edge of the vehicle and in the SBS at the same time. We can get results that service chaining offloading decision gives more optimal energy consumption with low-latency to autonomous vehicle users.

After successful development and industrial implementation of DX and DX+ Cell Technologies, EGA Technology Development launched several initiatives to lower CAPEX and cell energy consumption. The result is DX+ Ultra Technology, which is installed in five demonstration cells in the Eagle Section of Potline 5 Eagle at DUBAL (EGA Jebel Ali Operations), which were started up in March to May 2014 at 450 kA. The main new features of DX+ Ultra Technology are: reduced cell-to-cell distance as well as proprietary novel-design split anode risers, collector bar copper inserts and cathode flexes. More than one year of excellent performance at 455 kA with 95 % current efficiency and net specific energy consumption of 12.8 kWh/kg Al has confirmed that the technology is ready for industrial implementation. DX+ Ultra Technology has been selected for Alba’s Potline 6 expansion. Further optimization of the cells is underway to deliver best-in-class technology to the client. This includes larger busbar cross-sections for even lower energy consumption.

Optimising the energy consumption of robot movements has been one of the main focuses for most of today’s robotic simulation software. This optimisation is based on minimising a robot’s joints movements. In many cases, it does not take into consideration the dynamic features. Therefore, reducing energy consumption is still a challenging task and it involves studying the robot’s kinematic and dynamic models together with application requirements. This chapter explains how to minimise the robot energy consumption during assembly. Given a trajectory and based on the inverse kinematics and dynamics of a robot, a set of attainable configurations for the robot can be determined, perused by calculating the suitable forces and torques on the joints and links of the robot. The energy consumption is then calculated for each configuration and based on the assigned trajectory. The ones with the lowest energy consumption are selected. Given that the energy-efficient robot configurations lead to reduced overall energy consumption, this approach becomes instrumental and can be wrapped as a cloud service for energy-efficient robotic assembly.