Control Energy Consumption Research Articles

Data-Based Robust Adaptive Dynamic Programming for Balancing Control Performance and Energy Consumption in Wastewater Treatment Process

Data-Based Robust Adaptive Dynamic Programming for Balancing Control Performance and Energy Consumption in Wastewater Treatment Process

Research on improved partial format MFAC greenhouse temperature control method based on low energy consumption optimization

Temperature is critical to the growth of crops in agricultural greenhouses. Thus, designing a greenhouse temperature controller that maximizes energy savings while maintaining control accuracy is very important. This paper proposes an improved partial format model-free adaptive control method and designs a greenhouse temperature controller based on this method to balance control accuracy and energy consumption. Firstly, a limited energy consumption term is added to the control input cost function of the traditional partial format model-free adaptive control to penalize excessive control input. We derive an improved partial format model-free adaptive control input algorithm and design a greenhouse temperature controller using this algorithm. Then, the controller's sensitive parameters are selected using a Monte Carlo-based parameter sensitivity method. Finally, the whale optimization algorithm is used to optimize the sensitive parameters. The insensitive parameters are set according to experience. This paper, a theoretical study based on simulated experiments, proves the convergent tracking error of the proposed improved partial format model-free adaptive algorithm. Simulation results show that the improved controller minimizes energy consumption while ensuring control accuracy, reducing power consumption by 12.35 % compared to the traditional controller.

The Nexus Between Remittances, Natural Resources, Economic Growth, Healthcare and Environmental Sustainability In CIS Countries

Globally, the issues about sustainable development are on the increase, especially in emerging economies as CIS countries. Due to rising migration and remittances, natural resources are degraded, and economic expansion might pose serious challenges to the environment and healthcare. Thus, this research looks at how life expectancy is affected by economic growth, natural resources and carbon dioxide (CO2) emissions in 11 CIS countries (Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russia, Tajikistan, Ukraine, and Uzbekistan) by con-trolling energy consumption from 1990 to 2019. The Dumitrescu-Hurlin causality test discloses a cointegration between life expectancy, remittances, natural resources, economic growth, and CO2 Emissions. The empirical analysis indicates that life expectancy is positively correlated with re-mittances and GDP while water withdrawal and CO2 emissions have negative impact on life ex-pectancy in the case of CIS countries. Our empirical findings may provide insightful policy implications towards strengthening the public healthcare system. Therefore, appropriate policy responses can be developed towards advanced public healthcare and environmentally sustainable sources of energy in order to achieve the sustainable development goals.

Research on energy harvesting characteristics of a flapping foil with trailing edge jet flap

Flapping foils offer a potential means for extracting hydrokinetic energy from ocean and river flows by utilizing a combined pitching and heaving motion of the airfoil. In this paper, the effectiveness of trailing edge active flow control in enhancing the energy harvesting performance of the conventional flapping foil is examined through numerical simulations. In this approach, an injections slot is opened at the trailing edge of the flapping foil, from which a high velocity jet is discharged into ambient water. Since the water jet is issued vertically downwards perpendicular to the airfoil surface, it operates like a Gurney flap and thus is known as jet flap in aeronautics. Parametric studies show that the optimal injection position should be as far as possible from the leading edge of the flapping foil. In addition, it is also found when the width of the exit slot of jet equals to 2.6 % of the airfoil chord and the momentum coefficient of jet is 0.02, the flapping foil with the jet flap can increase the maximum net energy harvesting efficiency obtained after deducting the energy consumption for the flow control from 39.22 % to 47.6 % compared to its baseline counterpart, indicating the effectiveness of this method. Furthermore, the dynamic mode decomposition (DMD) method is also adopted to reveal the spatial-temporal features of the coherent structures in the wake flow behind the proposed flapping foil in an attempt to gain a deep understanding of the underlying flow mechanism involved in promoting its energy harvesting performance.

A new prediction method for sodium aluminate solution evaporation integrating process knowledge and data-driven spatial-temporal adaptive model

In alumina production, the evaporation as the key process uses recyclable resources and reduces environmental pollution. In fact, the quality of export product with offline and delayed, results in low precision of process control and high energy consumption. To ensure green and efficient production, in this paper, a new prediction method integrating process knowledge and data-driven spatial-temporal adaptive model is put forward. First, to preprocessed production data for ensuring modeling accuracy, data reconciliation technology is adopted. Then, based on material and heat transfer mechanism, for equipment and industrial process, the mechanism models are established. Furthermore, with time difference and moving window model, an error compensation method is utilized in terms of double locally weighted kernel PLS for estimation error in hypothesis-based mechanism modeling. Finally, the data-driven spatial-temporal adaptive model and the process knowledge-based mechanism model are integrated. To illustrate the model feasibility, an industrial sodium aluminate solution evaporation is used. It demonstrates that, for the developed model, the prediction accuracy can reach more than 90% within the ±2% error range, and effectively estimate the actual product quality and ensure the prediction effect.

Stabilization control of rotary inverted pendulum using a novel EKF-based fuzzy adaptive sliding-mode controller: design and experimental validation

This article methodically develops an improved self-regulating fuzzy-adaptive Sliding Mode Controller (SMC) that strengthens the disturbance compensation capacity of the nonlinear rotary pendulum systems while effectively attenuating the chattering content and curbing the control energy consumption. The article contributes to augmenting the SMC with online adaptation tools to achieve the said objectives. It employs the conventional Gao’s power-rate reaching law as the baseline. The scaling gain and the power rate of the said reaching law are adaptively modulated via a pre-calibrated two-input state-error-driven fuzzy nonlinear function. Additionally, the sign function in the law is also replaced with an odd-symmetric nonlinear fuzzy function to address the hard limits imposed by the former. Finally, the membership functions of the fuzzy function are self-regulated using the Extended-Kalman-Filter to improve the compensator’s adaptability in handling the system’s rapidly changing control requirements under exogenous disturbances. The aforementioned propositions are verified by performing customized and reliable hardware-in-loop experiments on the Quanser single-link rotary pendulum platform. As compared to baseline SMC law, the proposed control procedure contributes a ∼45.2%, ∼48.5%, and ∼34.8% reduction in position-regulation errors, control energy consumption, and peak overshoots, respectively. The experimental assessment validates the proposed control system’s enhanced robustness and chattering-suppression capability.

Motion-pressure coupled control and simulation of long-endurance capability for multicapsule stratospheric airships

The current study focuses on the motion-pressure coupled control for a multicapsule stratospheric airship and transforms the path-tracking and heading-hold control of airships into guidance tracking with a time-varying weighted sum of longitudinal and lateral velocities by the definition of compound speed. Herein, an improved nonlinear predictive control method is provided to reduce the control energy consumption by the rolling optimization of controller parameters based on finite time intervals, ensuring infinite-time path-tracking tasks. Simultaneously, combined with the proposed cyclic regulation process of safe pressure between internal and external capsules, this study can fully reflect the force-thermal coupled rule of airships under the actions of atmospheric environment and maneuvering force, while evaluating the long-endurance capability of airships under the conditions of safe superheating and overpressure. The effectiveness of the motion-pressure coupled controller was verified through numerical simulations, which can overcome the influence of environmental wind and achieve a tracking effect for the desired cruise path and compound speed. The airspeed provided during the cyclic circadian time caused the maximum superheating of the helium controlled within 30 °C. The helium in the internal and external capsules achieved circadian regulation. The equivalent micropore diameter of the capsule of 5 mm can achieve 55 days of long-endurance flight. The controller satisfies the requirements of cruise-flight application modes for multicapsule stratospheric airships with important engineering value.

Fuzzy Approach for Managing Renewable Energy Flows for DC-Microgrid with Composite PV-WT Generators and Energy Storage System

Recently, the implementation of software/hardware systems based on advanced artificial intelligence techniques for continuous monitoring of the electrical parameters of intelligent networks aimed at managing and controlling energy consumption has been of great interest. The contribution of this paper, starting from a recently studied DC-MG, fits into this context by proposing an intuitionistic fuzzy Takagi–Sugeno approach optimized for the energy management of isolated direct current microgrid systems consisting of a photovoltaic and a wind source. Furthermore, a lead-acid battery guarantees the stability of the DC bus while a hydrogen cell ensures the reliability of the system by avoiding blackout conditions and increasing interaction with the loads. The fuzzy rule bank, initially built using the expert’s knowledge, is optimized with the aforementioned procedure, maximizing external energy and minimizing consumption. The complete scheme, modeled using MatLab/Simulink, highlighted performance comparable to fuzzy Takagi–Sugeno systems optimized using a hybrid approach based on particle swarm optimization (to structure the antecedents of the rules) and minimum batch squares (to optimize the output).

Finite-time stochastic control for complex dynamical systems: The estimate for control time and energy consumption

Controlling complex dynamical systems has garnered significant attention within academic circles in recent decades. While most existing works have focused on closed-loop control schemes with infinite-time durations, this paper introduces a novel finite-time, closed-loop stochastic controller that considers control time, energy consumption, and their dependence on system parameters. This stochastic control technique not only enables finite-time control in chaotic dynamical systems but also facilitates finite-time synchronization in unidirectionally coupled systems. Importantly, our proposed scheme offers several advantages over existing deterministic finite-time controllers, particularly in terms of time and energy consumption. Through numerical experiments utilizing random ecosystems, neural networks, and Lorenz systems, we provide evidence of the effectiveness of our analytical results. We anticipate that this stochastic scheme will find wide-ranging applications in the control of complex dynamical systems and in achieving network synchronization.

Design and Development of an IoT Gateway for Smart Building Applications

Due to the depletion of energy resources and increased energy demand, there is an increased focus on the energy consumption and management in buildings. Many building energy management (BEM) software platforms are commercially available to monitor and control energy consumption. These platforms are hosted on the physical hardware within the building, due to which the hardware specifications limit their performance. To address this limitation, cloud technology emerged which facilitates software to be deployed at a remote location that has scalable hardware resources. Conventional BEM software can leverage such a cloud platform to offer scalable and maintenance-free installation. Once hosted on a remote cloud platform, BEM software lacks direct connectivity to building sensors/controllers, hence requires a device to support remote accessibility. Most devices are bound to a local area network and therefore need an additional functional layer on top of its communication stack to perform Network Address Translation (NAT)-Traversal. This functionality is implemented on a scalable software which connects to the devices in the network and acts as a gateway for cloud-based BEM software to access devices in the local area network. Any message sent to this gateway is translated to a respective device protocol. This paper describes the design and implementation of such an Internet of Things (IoT) gateway for a cloud-based BEM system that requires support for BACnet, Modbus, and HTTP RESTful interface devices.

Infectious diseases prevention and control with reduced energy consumption in an airport

The COVID-19 pandemic threatened the world. As an important transportation hub connecting countries and regions, airports have played a critical role in COVID-19 prevention and control. This study developed an infection risk-human comfort-energy consumption model to calculate the COVID-19 transmission and energy consumption for epidemic prevention and control in seven different functional areas of an airport during different seasons (winter, summer, and transition season) and actual passenger movement. When considering dynamic passenger flow, the energy consumption needed to prevent and control transmission of the epidemic in each area of the airport could be reduced by 71–85 %. The waiting, dining, and shopping areas were the areas with the highest energy consumption, accounting for 25–47 %, 15–32 %, and 11–38 % of the total energy consumption of epidemic prevention at the airport, respectively. The dining area had the highest energy consumption per square meter, reaching 14.2 kWh/m2 at its highest. After closing the dining area, energy consumption was reduced by 14–20 %. Compared with strict epidemic prevention and control, energy consumption in the airport was reduced by 70–85 % considering both optimized intervention and dynamic passenger flow. The results of this study provide a scientific basis for energy-saving and emission reduction in airports under an epidemic situation.

АНАЛИЗ СОВРЕМЕННЫХ ЭКОНОМИЧЕСКИ ЦЕЛЕСООБРАЗНЫХ ПОДХОДОВ В ПРОЕКТИРОВАНИИ СИСТЕМ ТЕПЛОСНАБЖЕНИЯ

The work is devoted to modern, energy-efficient, environmentally friendly, economically feasible methods used in the design of heat supply systems. The purpose of this article is to analyze new technologies and approaches that can significantly improve the efficiency of the heat supply system, reduce energy costs and minimize the negative impact on the environment. This goal was achieved through an extensive analysis of modern approaches in the design of heat supply systems through the use of alternative energy sources, including solar, wind, and geothermal energy. The most important result is the consideration of the use of smart technologies and control systems to optimize the operation of the heating system. Smart meters, monitoring and control systems allow you to effectively control energy consumption, prevent leaks and emergency situations, and optimize heat distribution in the network. The significance of the results obtained lies in determining the possibility of implementing an energy efficiency program, as well as reducing costs and minimizing harm to the environment. The practical implementation of these approaches will make it possible to create a sustainable and environmentally friendly heat supply system for our cities and settlements.

FUZZY FRACTIONAL ORDER SLIDING MODE CONTROL FOR OPTIMAL ENERGY CONSUMPTION-TRANSIENT RESPONSE TRADE-OFF IN ROBOTIC SYSTEMS

This work presents a new fuzzy logic parameter tuning-based fractional order sliding mode control (F-FOSMC) strategy for the trajectory tracking of robotic systems. The primary objective is to achieve an optimal control compromise seeking to minimize energy consumption for required motions, all while increasing both accuracy and rapidity of the transient response, addressing therefore a critical challenge in the field of robotics. Our approach integrates a sophisticated fuzzy logic fractional order mechanism to dynamically fine-tune the noninteger derivation parameter of the FOSMC, offering adaptability to varying operational conditions. This intelligent fuzzy logic controller tuning optimizes the FOSMC performance, demonstrates a remarkable capacity to handle uncertainties and disturbances intrinsic to robotic applications and allows to optimize the trade-off between the energy consumption and transient response efficiency throughout the robot’s motion. The effectiveness of the proposed study is thoroughly examined via simulations conducted on a 3 DOF manipulator robotic system. Results showcase that F-FOSMC significantly reduces control energy consumption while preserving rapid and efficient transient response characteristics.

Application of BIM technology in construction project cost refinement control and construction energy consumption control

Abstract In order to solve the problem of accurate cost control management in the process of engineering construction, this paper applies BIM technology to the whole process of cost management, covering the whole life cycle of the project. Through efficient sharing of information on the BIM collaborative platform, it carries out unimpeded process work and achieves refined management. Aiming at the complexity of the information characteristics that affect the cost and uncertainty of information transmission, an algorithm fusion scheme based on NCM-FPN has been designed. The TOPSIS method is used to integrate the case feature attributes into the cost case information database. By combining the clustering algorithm with practical management experience, the knowledge base of engineering cost control strategies can be established, and the logical relationship of strategy matching can be determined. The inverse cloud algorithm is used to establish the feature cloud, determine the fuzzy Petri network structure based on generative rules and design the inference algorithm to establish an intelligent model for strategy matching so as to realize the accurate mapping and rapid response from the target case features to the control strategies. Through the analysis of construction energy consumption, it is concluded that the construction energy consumption of different structure buildings is not the same; the construction energy consumption per unit area of brick structure, steel structure residence and frame office building is 0.1659GJ/m2, 0.2706GJ/m2 and 0.3104GJ/m2 respectively, and the largest is the shear-wall structure residence is 0.3381GJ/m2 for the 2.04 times of the brick structure. Based on BIM technology, the cost of dynamic control engineering for construction projects was reduced by 5.4 million. Therefore, BIM is an important technical means to promote the whole process control of project cost, from empirical control to informationization, standardization, and intelligent control.

Enhancing smart home appliance recognition with wavelet and scalogram analysis using data augmentation

The development of smart homes, equipped with devices connected to the Internet of Things (IoT), has opened up new possibilities to monitor and control energy consumption. In this context, non-intrusive load monitoring (NILM) techniques have emerged as a promising solution for the disaggregation of total energy consumption into the consumption of individual appliances. The classification of electrical appliances in a smart home remains a challenging task for machine learning algorithms. In the present study, we propose comparing and evaluating the performance of two different algorithms, namely Multi-Label K-Nearest Neighbors (MLkNN) and Convolutional Neural Networks (CNN), for NILM in two different scenarios: without and with data augmentation (DAUG). Our results show how the classification results can be better interpreted by generating a scalogram image from the power consumption signal data and processing it with CNNs. The results indicate that the CNN model with the proposed data augmentation performed significantly higher, obtaining a mean F1-score of 0.484 (an improvement of +0.234), better than the other methods. Additionally, after performing the Friedman statistical test, it indicates that it is significantly different from the other methods compared. Our proposed system can potentially reduce energy waste and promote more sustainable energy use in homes and buildings by providing personalized feedback and energy savings tips.

Multi-criteria evaluation of novel multi-objective model predictive control method for indoor thermal comfort

We proposed a novel multi-objective model predictive control (MPC) approach based on a straightforward internal prediction model to achieve building energy efficiency and maintain the indoor temperature within a predetermined comfort range. Using the CARNOT Toolbox, we built a detailed room model based on a real room with water-circulated radiator heating. We developed an MPC controller using MATLAB and combined it with the room model in the CARNOT Toolbox to tune the controller parameters and evaluate its performance. Based on the co-simulations, a control step of 15 min and a prediction horizon of 90 min were found to be suitable for room level indoor thermal comfort control. The performance of the controller was evaluated in terms of multiple criteria, including control accuracy, hydrodynamic stability, and energy consumption. Compared with the traditional proportional-integral-derivative (PID) control, the MPC demonstrated a 16.4 % improvement in control accuracy, 2.8 % lower energy consumption, and a 50 % reduction in the hot water flow change rate, improving the system’s hydrodynamic stability. A significant advantage of the MPC is that it is possible to compute different efficient solutions by modifying the parameters, among which the decision-makers can choose their most preferred compromise solution considering multiple criteria.

The Performance of Reinforcement Learning for Indoor Climate Control Devices according to the Level of Outdoor Air Particulate Matters

As people spend more than 90% of their time indoors, indoor environmental quality (IEQ) has become an important factor in maintaining a healthy space for the occupants. There are many indoor climate control devices for improving IEQ. However, it is difficult to maintain an appropriate IEQ with changing outdoor air conditions and occupant behavior in a building. This study proposes a reinforcement learning (RL) algorithm to maintain indoor air quality (IAQ) with low energy consumption in a residential environment by optimally operating indoor climate control devices such as ventilation systems, air purifiers, and kitchen hoods. The proposed artificial intelligence algorithm (AI2C2) employs DDQN (double deep Q-network) to determine the optimal operation of various indoor climate control devices, reflecting IAQ and energy consumption via different outdoor levels of particulate matter. This approach considers the outdoor air condition and occupant activities in training the developed algorithm, which are the most significant factors affecting IEQ and building energy performance. A co-simulation platform using Python and EnergyPlus is applied to train and evaluate the model. As a result, the proposed approach reduced energy consumption and maintained good IAQ. The developed RL algorithm for energy and IAQ showed different performances based on the outdoor PM 2.5 level. The results showed the RL-based control can be more effective when the outdoor PM 2.5 level is higher (or unhealthy) compared to moderate (or healthy) conditions.

Joint maintenance planning and production scheduling optimization model for green environment

PurposeThis research integrates maintenance planning and production scheduling from a green perspective to reduce the carbon footprint.Design/methodology/approachA mixed-integer nonlinear programming (MINLP) model is developed to study the relation between production makespan, energy consumption, maintenance actions and footprint, i.e. service level and sustainability measures. The speed scaling technique is used to control energy consumption, the capping policy is used to control CO2 footprint and preventive maintenance (PM) is used to keep the machine working in healthy conditions.FindingsIt was found that ignoring maintenance activities increases the schedule makespan by more than 21.80%, the total maintenance time required to keep the machine healthy by up to 75.33% and the CO2 footprint by 15%.Research limitations/implicationsThe proposed optimization model can simultaneously be used for maintenance planning, job scheduling and footprint minimization. Furthermore, it can be extended to consider other maintenance activities and production configurations, e.g. flow shop or job shop scheduling.Practical implicationsMaintenance planning, production scheduling and greenhouse gas (GHG) emissions are intertwined in the industry. The proposed model enhances the performance of the maintenance and production systems. Furthermore, it shows the value of conducting maintenance activities on the machine's availability and CO2 footprint.Originality/valueThis work contributes to the literature by combining maintenance planning, single-machine scheduling and environmental aspects in an integrated MINLP model. In addition, the model considers several practical features, such as machine-aging rate, speed scaling technique to control emissions, minimal repair (MR) and PM.

Study on integrated thermal management system of hydrogen fuel cell vehicles based on heat pump air conditioning

An integrated thermal management system (VITMS) of hydrogen fuel cell vehicle (FCV) is proposed to solve the issues of poor temperature control and high energy consumption in current dispersed TMS. The vehicle energy integration is realized by coupling the key subsystems of cabin, fuel cell, power battery and motor through the waste-heat source heat pump (WSHP). The temperature control capability under extreme working conditions are analyzed. The energy consumption of three heating modes under extreme cold conditions is evaluated. The results show that VITMS has excellent thermal control performance and can realize fourteen work modes such as cooling, heating, preheating, defrosting and energy recovery, ensuring the optimal temperature ranges and temperature uniformity of fuel cell, power battery, motor, and cabin. VITMS using WSHP offers superior energy saving effect in extreme cold conditions, which can reduce equivalent hydrogen consumption by 12.21% and 3.6%, and increase maximum driving range by 29.34% and 8% compared to PTC and air-source heat pump (ASHP). The cabin heating time using WSHP can be reduced by 34.5% compared to ASHP, ensuring all-weather ride comfort and extending the vehicle's driving range in low temperatures. Annual CO2 emission is 52.13% and 36.17% lower than diesel and electric buses.

Adaptive Optimal Control of UAV Brake Based on Parameter Estimation

A new parameter estimation method for a UAV braking system with unknown friction parameters is suggested. The unknown part containing friction is separated from the coupling system. The law of parameter updating is driven by the estimation error extracted by the auxiliary filter. A controller is developed to guarantee the convergence of the parameter estimation and tracking errors simultaneously. Furthermore, a designed performance index function enables the system to track the desired slip rate optimally. The optimal value of the performance function is updated by a single critic neural network (NN) to obtain comprehensive optimization of the control energy consumption, dynamic tracking error, and filtering error during braking. In addition, the unknown bounded interference and neural network approximation errors are compensated by robust integral terms. Simulation results are presented to confirm the effectiveness of the proposed control scheme.