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Encapsulation of the fine powder of ammonium molybdophosphate (AMP) was carried out to produce AMP-Ca-alginate composite beads for removal of Cs+, U6+, Co2+ and Sr2+ ions from multicomponent media using a fixed-bed column. The results denote that the initial concentration (50, 100 and 200 mg/L from each ion), bed length (3, 6 and 8 cm) and flow rates (0.5, 1.5 and 2 mL/min) affect the sorption operation. The column performance was 40, 37.5, 32.34 and 31.7% for Cs+, U6+, Co2+ and Sr2+, respectively. Thomas and Yoon-Nelson models were utilised to forecast the breakthrough curves for adsorption. The two models are found as well-fitted models for investigated ions adsorption system except for the adsorption of Cs+ is more fitted for the Thomas model at a high flow rate. Marquardt's percent standard deviation error function was used and confirmed these results. Good separation of strontium and caesium with a purity of 100% from this multi-component solution is accomplished by elution using 4 M of NaNO3 and HNO3 solution. The desorption percent of the loaded ions was 98, 91, 76 and 37.5% for Cs+, Sr2+, U6+ and Co2+, respectively. Overall, AMP-Ca-alginate composite beads are an environmentally friendly potential sorbent for hazardous ions from multicomponent media. [ABSTRACT FROM AUTHOR]

Sulfur dioxide is serious ultimatum to human health as well as environment, while carbon dioxide is viewed as one of the primary drivers of the worldwide temperature alteration. Therefore, capturing of these gases is a dynamic research subject attracting much consideration from scientists. Herein, we report synthesis of a series of Co‐ZIF and bimetallic M‐Co‐ZIF adsorbents and application for room temperature adsorption of SO2 and CO2. In this work, the breakthrough curves for the adsorption of sulfur dioxide and carbon dioxide on Co‐ZIF and M‐Co‐ZIF were obtained experimentally and theoretically using a laboratory‐scale fixed bed column at room temperature. In this work, the adsorption capacities and breakthrough points for modified bimetallic M‐Co‐ZIF were found to be relatively higher than parent Co‐ZIF. Notably, a high SO2 uptake capacity of 7.1 mmol/g for Zr‐Co‐ZIF and high CO2 uptake capacity of 69.9 mmol/g for Ni‐Co‐ZIF were achieved. The parent cobalt and bimetallic ZIF materials were characterized by XRD, FTIR, SEM, and nitrogen physisorption. The XRD results confirm the formation of pure phase highly crystalline ZIF materials while BET analysis suggests high surface area of prepared adsorbents. Finally, the results of dynamic adsorption combined with characterization show great potential for preparation of bimetallic ZIF adsorbents for effective SO2 and CO2 adsorption. [ABSTRACT FROM AUTHOR]

This paper aims to explore the economic impact of innovative entrepreneurship education and proposes a dynamic modelling method to quantify this impact. The paper establishes a theoretical analysis framework, starting from the effects of innovative entrepreneurship education on the regional economy, and establishes a corresponding dynamic economic model. This model comprises processes such as variable definition, relationship establishment, parameter setting and architecture construction. Different levels of education and forms of innovative entrepreneurship education were experimentally analysed from multiple perspectives. In the comparative experiment between innovative entrepreneurship education and traditional education, the entrepreneurial intention score of the innovative entrepreneurship education group was scattered in the range of 90–100, higher than the 60–80 score of the traditional education group. In the experimental survey on investment return rates of different forms of innovative entrepreneurship education, it was found that the investment return rate of education was the lowest, only 6.82%, and the return rate of entrepreneurship competition was the highest, reaching 9.19%. The experimental results have proven a positive correlation between innovative entrepreneurship education and economic growth. It is suggested that the government and enterprises should increase their investment in innovative entrepreneurship education to promote sustainable economic development. [ABSTRACT FROM AUTHOR]

ABSTRACT The study introduces three novel strategies for incorporating capabilities for dynamic modelling into multiblock regression methods by integrating sequentially orthogonalised partial least squares (SO‐PLS) with different dynamic modelling techniques. The study evaluates these strategies using synthetic datasets and an industrial example, comparing their performance in predictive ability, identification of process dynamics, and quantification of block contributions. Results suggest that these approaches can effectively model the dynamics with performance comparable to state‐of‐the‐art methods, providing, at the same time, insight into the dynamic order and block contributions. One of the strategies, sequentially orthogonalised dynamic augmented (SODA)–PLS, shows promise by ensuring that redundant information in the time dimension is not included, resulting in simpler and more easily interpretable dynamic models. These multiblock dynamic regression strategies have potential applications for improved process understanding in industrial settings, especially where multiple data sources and inherent time dynamics are present. [ABSTRACT FROM AUTHOR]

A simulation investigation of emergency evasive maneuvers of the type experienced during obstacle avoidance is conducted for long combination vehicles at highway speeds. The dynamic model includes a commercial truck with a configuration that is typical of what is used in the U.S., with two 28-ft trailers connected to the tractor through a fifth wheel, and to each other by an A-dolly. The model is validated against test data and used for evaluating distance and time needed to safely perform a steering maneuver to avoid an obstacle on the road. The simulation results indicate a significant difference in both the required time and distance for safely exercising an emergency obstacle avoidance, ranging from approximately 151–276 ft in distance and 1.7–2.4 s from the start of the driver action. The reaction time of the driver will add to both measures by an amount corresponding to the lapse in time between the moment the driver recognises the obstacle and when a steering action is initiated. To the extent that the lighting and road condition add to the reaction time, a significantly larger distance and longer time are needed during nighttime driving as compared with daytime, under similar road and loading conditions. [ABSTRACT FROM AUTHOR]

Integrating non‐conventional renewable energy sources into distribution systems, alongside data science and enabling technological infrastructures, presents significant challenges, particularly in managing active demand. The rapid evolution of the electric energy system and increasing electricity demand highlight the need for reliable tracking and predictive methods to manage Distributed Energy Resources and digital infrastructure. These methods are essential for advancing carbon neutrality, democratizing environmental sustainability, and improving energy efficiency. Effective active demand monitoring requires understanding the transactional system concept, including digital infrastructure and decentralized demand. Although metaheuristic techniques are increasingly important in demand response integration, much research focuses on specific techniques rather than providing a comprehensive view of dynamic transaction integration for active demand. Technological advancements, like smart meters and communication systems, are shifting from basic consumption measurement to active customer participation. This article reviews key concepts in electrical distribution systems, such as active demand, DERs, and transactive systems. It examines prevalent metaheuristic techniques, emphasizing their role in integrating and predicting active demand and DER behaviors. Additionally, the study presents a methodology serving as a roadmap for efficient DER integration and the transition to active demand and transactive electricity systems, addressing gaps in the current literature. [ABSTRACT FROM AUTHOR]

Bearing vibration characteristic analysis based on dynamic analytical model is of importance for identifying faults of bearing in mechanical systems. However, on one hand, the relationship between the raceway-ball contact pairs in bearing dynamic models are often considered as a simplified cascade; on the other hand, the evolving law of bearing fault characteristics of the early fault (wear) and severe fault (pitting) has not been fully revealed, which confines the development of bearing fault feature extraction and severity assessment methods. This paper, therefore, constructs a dynamic analytical model considering time-varying contact pairs and parameterized defects of deep groove ball bearings (DGBB) to illustrate the dynamic vibration characteristics of DGBB under different health conditions. The degrees of freedom of balls and flexible cage, multi-parameter defect excitation, mixed-elastohydrodynamic lubrication (mix-EHL), and relative slippage are all considered in the proposed model. Positions of multiple rigid bodies and contact points are dynamically expressed by coordinate systems, from which system physical parameters and geometrical relationship can be obtained. The bearing early and severe fault excitations are described by multi-parameters. The early fault which is embodied as surface roughness yields the indirect displacement excitation and the severe fault yields the direct displacement excitation. The surface roughness and indirect displacement excitation affect the mix-EHL, the radial force and friction force of contact pairs, thereby affecting the vibration response of DGBB. The evolving mechanism analysis of bearing fault characteristics are then revealed based on the established dynamic model, indicating the coupling and nonlinear relationship between the characteristic frequency amplitude and the fault severity of DGBB. [ABSTRACT FROM AUTHOR]

A novel model reduction method for viscously damped structures with statically determinate interfaces, such as spacecraft flexible appendages, is proposed. The paper presents a derivation of the complete complex modal expansion of the interface dynamic stiffness of these structures. Based on the identity relation for all complex modes, which is obtained during the derivation, it is found that the interface acceleration impedance can be expressed as a rational fraction with high accuracy using only low-order complex modes. Using this rational fraction as an approximation model, numerical results of the frequency response can be fitted. The fitted interface acceleration impedance can be applied to real-time control as a reduced model in the form of a transfer function. Furthermore, it can be transformed into the form of system matrices by introducing auxiliary variables, which then participate in the dynamic analysis of the assembly. The reduction process circumvents complex modal analysis and necessitates only the results of frequency responses. Thanks to the powerful ability of conventional finite element software to perform frequency response analysis, this reduction method can be used for large-scale complex models in actual engineering applications. [ABSTRACT FROM AUTHOR]

Undoubtedly, wind turbines are currently one of the most significant contributors to clean energy. Therefore, it is crucial to enhance the capability of wind turbines, which in turn leads to an increase in their dimensions. Nevertheless, only advanced control systems can guarantee the optimal and secure operation of these huge machines. On the other hand, the precise control of these modern wind turbines is only achievable through the use of highly specialised actuators. Despite their importance, actuators have historically been overlooked and seen as secondary components in control systems. However, in modern machines, actuators are required to manipulate multiple tonnes or manage thousands of volts and amperes within short times. Consequently, greater emphasis must be placed on their handling and operation. This study aims to review actuators for modern large wind energy converters from a control engineering perspective, using a tutorial approach. [ABSTRACT FROM AUTHOR]

The demand for cement has significantly increased, growing by 8% in the year 2022 and by a further 12% in 2023. It is highly anticipated that this trend will continue, and it will result in significant growth by 2030. However, cement production is highly energy-intensive, with 70 to 80% of the total energy consumed during the clinker formation, which is the main cement production process. Minimising energy losses requires a radical approach that includes optimising the performance of the kilns and significantly improving their energy efficiency. One of the most efficient approaches to optimise the performance of the kilns and reduce energy losses is by integrating process re-engineering models, which leverage process data analytics, machine learning, and computational methods. This study employed a model-based integration approach to improve energy efficiency during clinker formation. Energy consumption data were collected from two semi-automated cement production plants. The data were analysed using a regression model in Minitab (Minitab 21.1.0) statistical software. The analysis resulted in a linear energy consumption equation that links energy consumption to both production and energy loss. Dynamic simulations and modelling using Simulink in MATLAB were performed based on a proportional–integral–derivative (PID)-controlled system. The dynamic behaviour of the model was evaluated using data from Plant A and validated with data from Plant B. The energy efficiency equation was established as a mathematical model that explains energy improvements based on incorporating parameters for the cement kiln system and disturbances from the environment. [ABSTRACT FROM AUTHOR]

Turntable ladders are flexible large-scale manipulators, so an active oscillation damping is highly valuable. Aiming to support the development and parametrization of the controller, a precise dynamic model of the vehicle is desired. Previous work considering solely the dynamic oscillations of the ladder parts is extended by a dynamic model of the chassis and support structure that includes flexible deformation of the chassis. All contact forces to the ground are calculated because they provide potential for determining dynamic outreach limits in future. As it can happen in practice, the outriggers are able to become fully unloaded in the simulation model. This behaviour is validated by measurements. A sensitivity analysis is used to establish a suitable approach for identification of model parameters. Dynamical measurements illustrate that the combination of two dynamic models of ladder and chassis improves representation of the dynamic oscillations that are crucial for the active oscillation damping control.

Abstract The magnet-less switched reluctance motor (SRM) speed-torque characteristics are ideally suited for traction motor drive characteristics and its advantage to minimize the overall cost of on-road EVs. The main drawbacks are torque and flux ripple, which have produced high in low-speed operation. However, the emerging direct torque control (DTC) operated magnitude flux and torque estimation with voltage vectors (VVs) gives high torque ripples due to the selection of effective switching states and sector partition accuracy. On the other hand, the existing model predictive control (MPC) with multiple objective and optimization weighting factors produces high torque ripples due to the system dynamics and constraints. Therefore, existing DTC and MPC can result in high torque ripples. This paper proposed a finite set (FS)-MPC with a single cost function objective without weighting factor: the predicted torque considered to evaluate VVs to minimize the ripples further. The selected optimal VV minimizes the SRM drive torque and flux ripples in steady and dynamic state behaviour. The classical DTC and proposed model were developed, and simulation results were verified using MATLAB/Simulink. The proposed model operated in SRM drives experimental results to prove the effective minimization of torque and flux ripples compared to the existing DTC.

This work discussed the detailed dynamic model of a Hydraulic Energy Storage based Hybrid Power Transmission (HESbHPT) technology. It is an unite operation of two individual power transmission systems, such as gear train and a state-of-art hydraulic energy storage-based hydraulic power transmission. The output power of the turbine rotor is applied as the input power of the gear train whereas the output power of the gear train is supplied to the hydraulic pump that supplies flow to the hydraulic motor of the hydraulic power transmission (HPT). A hydraulic energy storage, i.e., an accumulator and two control valves are incorporated between the hydraulic pump and the motor in order to operate the fixed load by storing or releasing the excess flow supplied from the pump. A Two-step or On-Off controller controls the control valves and ensures the charging and discharging operation of the accumulator. The dynamic bond graph model of the HESbHPT is developed and simulated using SYMBOLS SHAKTI software. The simulation responses of the proposed model indicate that the output speed of the hydro-motor is stable to 8.2 rad/s although the input power to the gear train is variable in nature. Also, the efficiency of the HESbHPT is reported, and found that the overall transmission efficiency is in the range of 90- 98%. Moreover, the pressure and energy storage variation into the accumulator is reported. [ABSTRACT FROM AUTHOR]

The optimization of novel aircraft Environmental Control System (ECS) architectures necessitates the support of complex numerical simulations. Within the framework of the more electric aircraft approach, a promising strategy to enhance the overall efficiency of traditional ECS involves the incorporation of vapor compression systems (VCSs), which offer superior cooling efficiency compared to conventional air cycle machines. In this study, a Modelica/Dymola library for simulating VCSs has been developed, with a focused emphasis on three critical attributes: numerical robustness, accuracy, and minimal computational time. To successfully integrate and simulate the VCS model into an entire ECS model, these characteristics are essential. An appropriate tradeoff has been achieved between the computational cost of the VCS model and its ability to accurately predict the system's physical behavior (the heat exchangers have been modeled using a partial dynamic approach to optimize both robustness and computational efficiency). The VCS model has undergone thorough testing and has been seamlessly integrated into a representative ECS model to replicate a typical commercial airplane flight mission. A comparative analysis of the advantages offered by a variable-speed compressor versus a fixed-speed one has been conducted, revealing potential energy savings of up to 40%. Furthermore, the incorporation of an internal heat exchanger has led to additional energy savings exceeding 5%. [ABSTRACT FROM AUTHOR]

With the increasing application of flexible pipes in deep-sea operations, higher processing accuracy is required for the individual metal layers of these pipes to withstand greater service loads. In the helical winding production of the tensile armour layer, the accuracy of tension control significantly affects the uniformity of the steel strip after winding. To ensure tensile stability during the processing of steel strips, a tensile model for the manufacturing process is proposed. This model can be used to predict the magnitude and trend of tension fluctuations caused by the unwinding device, facilitating the design of a constant tension control system for steel strips based on an integral separation fuzzy PID algorithm. This system can achieve rapid convergence of tension during large tension fluctuations. The experimental results confirm the accuracy of the prediction model and the reliability and effectiveness of the designed tension control system. With this system, the tension fluctuation range is reduced by nearly 40%, and the average value of the standard deviation of the spacing after steel strip winding is decreased by 0.21 mm, demonstrating the potential of this approach for improving the manufacturing quality of the tensile armour layer of flexible pipes. [ABSTRACT FROM AUTHOR]

Flexible links possess numerous advantages over rigid links which impose the research attention towards employing flexible links in robotics. However, flexible links have a tip vibration that has the difficulty of being presented in dynamic models. Furthermore, the tip vibration leads to the control difficulty as a controller must control a system for the position and the tip elastic vibration. This research proposes a dynamic model and an intelligent hybrid optimal controller of a hybrid manipulator, the hybrid manipulator has a rigid link, a flexible link, and a variable payload. The proposed dynamic model is obtained using a combined technique of the finite element method and the Lagrangian method. The proposed dynamic model is compared with a SimMechanics model in terms of their open-loop responses, and both models have close match responses which indicates that the novel dynamic model is accurate. The intelligent hybrid optimal controller is also proposed based on the integration of an optimal linear quadratic regulator and a fuzzy logic controller. The integration of the fuzzy control and the linear quadratic regulator controller achieves 39.5% reduction of the fuzzy rules which assists to solve the explosion of the fuzzy rules problem since the hybrid manipulator is a multi-input system. The proposed intelligent optimal controller demonstrates improved position control, effective tip vibration suppression, better tip trajectory tracking, and better robustness to overcome the impact of the payload uncertainty compared to the linear quadratic regulator controller based on the simulation validation of the Simulink in MATLAB. [ABSTRACT FROM AUTHOR]

Pneumatic artificial muscles (PAMs) possess compliant properties desirable for certain applications such as prosthetics and robotic structures. However, this compliance along with their inherent nonlinear dynamics make them difficult to accurately model and as such accurately control under certain control architectures. Common approaches to this problem include measuring the actuator's physical properties and approximating a model based on these parameters or using deep learning methods to train a model with the actuator's behaviours. This paper introduces an optimisation-based modelling approach based on a particle swarm optimisation (PSO) algorithm using a mass–spring–damper approximation for the PAM, as well as a piecewise modelling method that accounts for nonlinear dynamics. The use of optimisation to estimate model parameters removes the need to measure physical properties, and the three-element approximation allows for fast model generation with low computational complexity and training data requirements. Through multiple tests comparing model behaviour with real PAM motion, the accuracy of these models is confirmed to be promising for future work. Dynamic nonlinearities are properly accounted for using the piecewise modelling method, including both hysteresis and disproportionate input/output relationship across the stroke length of the actuator. Compared with other PAM modelling techniques, this method has improved generation time, lower computational load requirements, and complexity and can be applied to actuators for which the phenomenological mass–spring–damper model is a good approximation. [ABSTRACT FROM AUTHOR]

The magnet-less switched reluctance motor (SRM) speed-torque characteristics are ideally suited for traction motor drive characteristics and its advantage to minimize the overall cost of on-road EVs. The main drawbacks are torque and flux ripple, which have produced high in low-speed operation. However, the emerging direct torque control (DTC) operated magnitude flux and torque estimation with voltage vectors (VVs) gives high torque ripples due to the selection of effective switching states and sector partition accuracy. On the other hand, the existing model predictive control (MPC) with multiple objective and optimization weighting factors produces high torque ripples due to the system dynamics and constraints. Therefore, existing DTC and MPC can result in high torque ripples. This paper proposed a finite set (FS)-MPC with a single cost function objective without weighting factor: the predicted torque considered to evaluate VVs to minimize the ripples further. The selected optimal VV minimizes the SRM drive torque and flux ripples in steady and dynamic state behaviour. The classical DTC and proposed model were developed, and simulation results were verified using MATLAB/Simulink. The proposed model operated in SRM drives experimental results to prove the effective minimization of torque and flux ripples compared to the existing DTC. [ABSTRACT FROM AUTHOR]

Summary: The switched reluctance motor (SRM) is a prominent electrical machine drive with low cost and good performance in different drive applications. This machine's main drawbacks are torque ripple and flux ripple. However, the existing SRM‐operated direct torque control (DTC) Voltage vectors (VVs) give high torque ripples due to improper sector division and minimum selection of VVs switching states. On the other hand, a two‐level hysteresis torque band can result in torque ripples. Therefore, this paper proposed DTC using 16 partitions, active small and large VVs, and a multilevel hysteresis torque band (MHTB) strategy to mitigate the torque ripple further. More active VVs (i.e., 16) are employed in the modified sector‐based switching tables, suppressing the torque ripples. The proposed strategy is verified and validated using MATLAB/Simulink. The detailed result discusses the response of torque, flux, and speed of SRM. The DTC‐operated SRM drive experimental results prove the effective minimization of torque ripples in the proposed DTC compared to the existing DTC. [ABSTRACT FROM AUTHOR]

The most important task of ensuring the quality of operation of technological and research equipment is its effective protection from external vibration effects in the area of low frequencies, at which resonance phenomena are manifested. For this purpose, various types of vibration isolation systems are used, which are divided into passive and active. Passive systems effectively suppress vibrations at frequencies above 40-50 Hz, at lower frequencies such systems are ineffective as they cannot compensate for resonance phenomena. In this case, active vibration isolation systems are used. The active dampers and platform based on magnetorheological (MR) elastomers presented in the paper demonstrated higher vibration isolation efficiency in the frequency range of 0.3-3 Hz compared to the piezoelectric system and in the frequency range of 0.3-20 Hz compared to the platform based on electromagnetic power drive. At these frequencies, the vibration displacement amplitude transfer coefficient was less than 0.075. The use of MR effect makes it possible to regulate the stiffness and deformation of the elastic active element made of MR elastomer by changing the magnitude of magnetic induction. In this case it is possible to control dynamic and precision characteristics of the active damper, as well as to increase the efficiency of vibration isolation. During dynamic modelling of the active MP damper the differential equations and transfer functions of its elements were determined. Modelling in Simulink MATLAB software environment allowed to determine the transient process of damper movement under the influence of harmonic oscillations, select the type of regulator and calculate its tuning parameters. Experimental studies were carried out on a vibration test bench and confirmed the modelling results at frequencies of 0.3-50 Hz. At frequencies higher than 50 Hz passive vibration isolation begins to prevail due to absorption of vibration energy in the MR elastomer, which is not taken into account in the modelling. The experimental amplitudefrequency response of the platform showed high vibration isolation efficiency at frequencies of 0.3-100 Hz with vibration displacement amplitude transfer coefficient less than 0.075. [ABSTRACT FROM AUTHOR]

The breeding of forest tree species in the Baltic region has notably contributed to wood production for the bioeconomy. Growth modelling is used for long-term estimates of forest development. However, usually based on data from unimproved stands, they may underestimate the growth of improved trees. Accordingly, it is important to identify and integrate the altered stand dynamics associated with improved planting stock into existing growth models to accurately capture the resulting gains or, alternatively, develop new functions specifically designed for improved trees. We used the generalized algebraic difference approach to model and analyze height growth patterns of Scots pine and silver birch with different genetic improvement levels (improved forest reproductive material categories 'qualified' and 'tested'). Modelling was based on 14 260 and 55 926 height–age series from open-pollinated progeny trials in Latvia and Lithuania with an age range of 3–46 and 5–22 years for pine and birch, respectively. Dynamic generalized algebraic difference approach forms of commonly applied height growth functions with forest reproductive material-category-specific sets of coefficients were tested. The dynamic form of the Chapman–Richards and King–Prodan equations had the best fit for Scots pine and silver birch, respectively. The expected height growth of the category 'tested' was slightly better than the one for 'qualified', with more distinct differences in silver birch. The model with forest reproductive material-category-specific coefficients reflected the actual growth of improved trees; however, such application is limited to sites with medium and high site indices, where improved planting stock is typically used. We recommend the model for young stands up to the age of 20 and 40 years for pine and birch, respectively, considering the empirical data cover on which the functions are based. A unified dynamic height model with the same functional form but category-specific parameterization for different improvement levels allows a practical applicability and effective communication amongst diverse users, thereby promoting the utilization of the model amongst forest owners and managers who possess information regarding the origin of planting stock. [ABSTRACT FROM AUTHOR]

Steam condensers are critical for maintaining efficiency and stability in thermal power plants by converting exhaust steam back into water. Controlling the pressure within the condenser is essential for optimal performance and operational safety. This paper presents a dynamic model of a steam condenser, with pressure control achieved through a proportional-integral (PI) controller. The PI controller parameters are optimized using the gazelle optimization algorithm (GOA), which is applied for the first time to this system in the literature. Additionally, novel enhancements, the logarithmic spiral (Ls) search with greedy selection, are integrated into GOA to further enhance control effectiveness. The performance of the optimized PI controller is evaluated using nonlinear simulations, which include statistical analysis and fitness function assessments. To validate the effectiveness of the proposed approach, comparisons are made with other widely used optimization techniques. The results reveal that the GOA notably enhances control accuracy and stability, with the enhanced GOA (Ls-GOA) achieving the most significant improvements. This underscores its potential for enhancing steam condenser efficiency in thermal power plants.

Abstract Integrating non‐conventional renewable energy sources into distribution systems, alongside data science and enabling technological infrastructures, presents significant challenges, particularly in managing active demand. The rapid evolution of the electric energy system and increasing electricity demand highlight the need for reliable tracking and predictive methods to manage Distributed Energy Resources and digital infrastructure. These methods are essential for advancing carbon neutrality, democratizing environmental sustainability, and improving energy efficiency. Effective active demand monitoring requires understanding the transactional system concept, including digital infrastructure and decentralized demand. Although metaheuristic techniques are increasingly important in demand response integration, much research focuses on specific techniques rather than providing a comprehensive view of dynamic transaction integration for active demand. Technological advancements, like smart meters and communication systems, are shifting from basic consumption measurement to active customer participation. This article reviews key concepts in electrical distribution systems, such as active demand, DERs, and transactive systems. It examines prevalent metaheuristic techniques, emphasizing their role in integrating and predicting active demand and DER behaviors. Additionally, the study presents a methodology serving as a roadmap for efficient DER integration and the transition to active demand and transactive electricity systems, addressing gaps in the current literature.

The robust rotor structure and fault-tolerance characteristics of the Switched Reluctance Motors (SRMs) are the best choice for next-generation Electric Vehicle (EV) applications. This machine has few restraints like high torque and flux ripples. However, the existing Model Predictive Control (MPC) using multiple control objectives and maximum sectors in the switching table results in high torque ripples due to the improper sector partition, Voltage Vectors (VVs) and weight factor (K1) selection. This paper proposes a Finite Set-Model Predictive Control (FS-MPC) for an analytical model of a non-linearity SRM machine to analyze the torque ripple performance. The proposed VVs are derived using sector partition based on the rotor position. The control is designed as a single cost function with the weighting factor contributing to smooth torque by selecting optimal control signals. Simulation studies and experiments with a four-phase 8/6 SRM drive verifies the enhanced FS-MPC for real-time implementation. The dynamic speed and ripple values of SRM Drives are measured using a mixed signal oscilloscope and the sensor probes. The laboratory outcomes calculate the analytical equations to validate the findings. The calculated value of torque ripple is 9 % through this FS-MPC. The study reveals that the proposed method is well suited for torque ripple reduction than flux ripples.

The urgent need to achieve net-zero carbon emissions by 2050 has led to a growing focus on innovative approaches to producing, storing, and consuming energy. Integrated energy systems (IES) have emerged as a promising solution, capitalising on synergies between energy networks and enhancing efficiency. Such a holistic approach enables the integration of renewable energy sources and flexibility provision from one energy network to another, reducing emissions while facilitating strategies for operational optimisation of energy systems. However, emphasis has been mostly made on steady-state methodologies, with a dynamic verification of the optimal solutions not given sufficient attention. To contribute towards bridging this research gap, a methodology to verify the outcomes of an optimisation algorithm is presented in this paper. The methodology has been applied to assess the operation of a civic building in the UK dedicated to health services. This has been done making use of real energy demand data. Optimisation is aimed at improving power dispatch of the energy system by minimising operational costs and carbon emissions. To quantify potential discrepancies in power flows and operational costs obtained from the optimisation, a dynamic model of the IES that better captures real-world system operation is employed. By incorporating slow transients of thermal systems, control loops, and non-linearity of components in the dynamic model, often overlooked in traditional optimisation modules, the methodology provides a more accurate assessment of energy consumption and operational costs. The effectiveness of the methodology is assessed through model-in-the- loop co-simulations between MATLAB/Simulink and Apros alongside a series of scenarios. Results indicate significant discrepancies in power flows and operational costs between the optimisation and the dynamic model. These findings illustrate potential limitations of conventional operational optimisation modules in addressing real-world complexities, emphasising the significance of dynamic verification methods for informed energy management and decision-planning. [ABSTRACT FROM AUTHOR]

A 1D model describing the dynamics of an injection moulding machine and the injection process is presented. The model describes an injection cylinder actuated by a dual-pump electro–hydraulic speed-variable drive and the filling, holding and cooling phases of the injection moulding process utilising amorphous polymers. The model is suggested as the foundation for the design of model-based pressure controllers of, e.g., the nozzle pressure. The focus is on using material, mould and machine properties to construct the model, making it possible to analyse and design the dynamic system prior to manufacturing hardware or conducting experiments. Both the presented model and the developed controller show good agreement with experimental results. The proposed method is general in nature and enables the design, analysis and evaluation of the machine, material and mould dynamics for controller design based solely on the physical properties of the system. [ABSTRACT FROM AUTHOR]

Bolted spigot joints are widely used connection types in rotating machines, such as the rotor system of aero-engines and combustion gas turbines. Due to the nonlinearity of stiffness and damping caused by the bolted joint and mating interface friction, the dynamic performance of such a connection is too complicated to model. Therefore, the modelling and analysis of bolted spigot joints considering mating interface friction have become of interest in relevant investigations at present, which are of significance for predictions focusing on vibration performance. In this paper, the stick–slip behaviour and hysteresis characteristics of bolted spigot joints caused by mating interface friction are clarified by performing a study with a finite contact element model. Then, a new Iwan-based numerical model of bolted joints is proposed and an effective discretized parameter identification method is developed using the force–displacement results corresponding to the loading process obtained from finite element (FE) simulation results. A comparison study of the established numerical model and the FE simulation results demonstrates the potential of this numerical model to capture the static hysteresis behaviour and vibration performance. Then, the influences of the preload and loading amplitude of bolted joints on the static hysteresis behaviour and vibration performance of the system are examined using the numerical model. Finally, utilizing the results of bolted joint vibration response experiments performed with the established vibration test platform, the results of the numerical simulation are verified. The present study provides a theoretical basis for the design of the joint structure and assembly technology, which further enhances its prediction accuracy of dynamic performance. [ABSTRACT FROM AUTHOR]

Many failures of semiconductor-based oxide confined vertical cavity surface emitting lasers (VCSELs) are closely related to the generation and expansion of defects in the device structure. However, existing research has predominantly focused on the static study of defect morphology, with little attention given to analyzing the dynamic process of defect expansion, which limited our ability to predict device random failures due to lack of understandings on defect generation and expansion. To address this issue, we present a macroscopic phenomenological evolution model that describes the dynamic expansion of defects in VCSELs, in which the expansion of defects is treated as an anisotropic lattice strain diffusion process. We further exploit a diffusion-limited aggregation (DLA) method in solving the diffusion equation, which describes the random propagation and aggregation of strain in the vicinity of highly strained areas, resulting in defect formation when the stress from accumulated strain surpasses the bonding force of the atoms in the lattice. Our simulation result manages to replicate the dendritic expansion morphology of defects, aligning with experimental observations very well. Our model also predicts an accelerated defect expansion process, which is again consistent with the experimental result. This model finds relevance in applications such as random failure prediction through device aging, burn-in condition setting in device screening, and device structural and/or material design improvement for mitigating defect expansion.

Undoubtedly, wind turbines are currently one of the most significant contributors to clean energy. Therefore, it is crucial to enhance the capability of wind turbines, which in turn leads to an increase in their dimensions. Nevertheless, only advanced control systems can guarantee the optimal and secure operation of these huge machines. On the other hand, the precise control of these modern wind turbines is only achievable through the use of highly specialised actuators. Despite their importance, actuators have historically been overlooked and seen as secondary components in control systems. However, in modern machines, actuators are required to manipulate multiple tonnes or manage thousands of volts and amperes within short times. Consequently, greater emphasis must be placed on their handling and operation. This study aims to review actuators for modern large wind energy converters from a control engineering perspective, using a tutorial approach.

The demand for cement has significantly increased, growing by 8% in the year 2022 and by a further 12% in 2023. It is highly anticipated that this trend will continue, and it will result in significant growth by 2030. However, cement production is highly energy-intensive, with 70 to 80% of the total energy consumed during the clinker formation, which is the main cement production process. Minimising energy losses requires a radical approach that includes optimising the performance of the kilns and significantly improving their energy efficiency. One of the most efficient approaches to optimise the performance of the kilns and reduce energy losses is by integrating process re-engineering models, which leverage process data analytics, machine learning, and computational methods. This study employed a model-based integration approach to improve energy efficiency during clinker formation. Energy consumption data were collected from two semi-automated cement production plants. The data were analysed using a regression model in Minitab (Minitab 21.1.0) statistical software. The analysis resulted in a linear energy consumption equation that links energy consumption to both production and energy loss. Dynamic simulations and modelling using Simulink in MATLAB were performed based on a proportional–integral–derivative (PID)-controlled system. The dynamic behaviour of the model was evaluated using data from Plant A and validated with data from Plant B. The energy efficiency equation was established as a mathematical model that explains energy improvements based on incorporating parameters for the cement kiln system and disturbances from the environment.

Produced water (PW) is the largest by-product that comes out of the oil wells during oil and gas (O&G) field exploration. PW contains high-salt concentration along with other organic and inorganic components; therefore, PW must be treated before disposal. Electrocoagulation (EC) is an effective treatment method to remove pollutants from PW which has been the focus of many experimental studies; however, a mathematical model specifically for PW treatment by EC has not been developed yet. In this work, a comprehensive mathematical model has been developed to elucidate the role of EC operating parameters on the PW treatment performance and determine the mechanism for COD (Chemical Oxygen Demand) removal. The present model considers and identifies the dominant Al-hydroxy complex species and their contribution to the COD removal from synthetic PW samples by estimating their rate constants and comparing their magnitudes and investigates multi-scale modelling of the EC reactor. The influence of working parameters such as current density, initial pH, interelectrode distance, mixing speed and solution volume of PW on Al coagulant production and COD removal was investigated and modelled. The study estimates the rate constants of the reactions taking place for COD removal by EC process and by comparing their magnitudes identifies the dominant reactions and coagulant species involved in the process. The mathematical model prediction of COD removal fits well with the experimental data at 10 mA cm−2, 15 mA cm−2 and 20 mA cm−2 current density with R2 value of 0.96, 0.97 and 0.92, respectively and for dissolved Al concentration R2 value of 0.96, 0.99, and 0.97, respectively. The simulated results reproduced a good fit at initial pH of 6.1, 7.3 and 8.6 with R2 value of 0.92, 0.96 and 0.98, respectively for COD removal. The mathematical model and the experimental results showed the role of dominant Al-hydroxy complex species such as Al OH 2 + , Al OH 2 + , Al OH 3 , Al 2 OH 2 + 4 and Al OH 4 - in controlling the COD removal process. Under different operating conditions considered in the study, the model also predicted the COD removal performance of the EC reactors at different reactor volumes with R2 value of 0.96 for higher solution volume and larger reactor. The model presented and rate constants determined in the study will provide a theoretical basis for designing, scaling up and operating the EC reactor for oil-field PW treatment. [ABSTRACT FROM AUTHOR]

High-speed pick-and-place industrial applications often use parallel kinematic robots due to their high stiffness and dynamic performance; furthermore, the latter not only depends on the mechanical characteristics of the robots but also on the control algorithm. The literature shows several theoretical contributions to such controllers, mainly tested at the simulation level or on simple proof-of-concept laboratory equipment that execute low-speed and simple trajectories. This paper presents an experimental investigation of the dynamic performance of an industrial high-speed 4-DOF 5R parallel robot designed for pick-and-place applications on moving objects. The inverse dynamics control in the task space is used as a control algorithm. The results show the contribution of all the components of the control algorithm to the motor torque, and the inverse dynamics controller performances are discussed also in comparison to those achievable with simpler PD or PID controllers in a joint space. Moreover, the paper shows the controller synthesis from a modern mechatronic point of view, and the effectiveness of the proposed solution for the tracking of complex high-speed trajectories in an industrial application. [ABSTRACT FROM AUTHOR]

This paper presents a new method for the dynamics of multibody systems based on unknown constraint force. The method can uniformly solve multibody systems with typical configurations, including the system with rigid-flexible coupling, the system in tree topology, and the system with loop constraints. Unlike common methods, the proposed method can model the loop system without "cutting off" loop constraints, leading to the exact same modelling process as the tree-like system performs. Based on graph theory, a topological record matrix Mrec is proposed to capture the arbitrary system configuration. Moreover, constraint forces are selected as the key variables in semi-recursive framework. With the recursive kinematics relationship between adjacent bodies, the constraint force equation is further assembled to achieve the full-state system solution. The numerical simulations demonstrate the accuracy of the proposed method. [ABSTRACT FROM AUTHOR]