Dynamic Operation Research Articles

AbSolut - Integration of Absorption Technologies in District Heating and Cooling Systems for Enhanced Economic and Ecological Impact

District heating (DH) systems play a crucial role in meeting heating demands across the European Union (EU) and Austria, with significant potential for energy efficiency improvements and decarbonization. However, the transition towards climate neutrality by 2040 poses significant challenges, particularly in decarbonizing existing DH systems and integrating renewable energy sources. This work explores the application of absorption technologies, specifically absorption heat exchangers (AHX), absorption chillers (AC), and absorption heat pumps (AHP), in optimizing DH systems. The study investigates the utilization of AHX as transfer substations to increase heat capacity within existing grids by up to 30%, facilitating the integration of renewables and reducing distribution heat losses. Additionally, AC implementation for cooling supply demonstrates efficiency improvements through dynamic operation modes, renewable energy integration, and reduced electricity demand. Furthermore, AHP for waste heat utilization in DH power plants showcases environmental benefits, cost savings, and enhanced energy security. Through detailed techno-economic analyses and case studies, the paper evaluates the viability and economic feasibility of absorption technologies in DH applications. Challenges such as system integration, spatial requirements, and driving energy optimization are addressed, offering insights into overcoming barriers to adoption. Overall, the research highlights the transformative potential of absorption technologies in enhancing the efficiency, sustainability, and resilience of DH systems. By leveraging these technologies, DH operators and stakeholders can navigate the transition towards climate neutrality, while ensuring reliable and cost-effective heating and cooling solutions for urban areas.

DCFH: A dynamic clustering approach based on fire hawk optimizer in flying ad hoc networks

In flying ad hoc networks (FANETs), unmanned aerial vehicles (UAVs) communicate with each other without any fixed infrastructure. Because of frequent topological changes, instability of wireless communication, three-dimensional movement of UAVs, and limited resources, especially energy, FANETs deal with many challenges, especially the instability of UAV swarms. One solution to address these problems is clustering because it maintains network performance and increases scalability. In this paper, a dynamic clustering scheme based on fire hawk optimizer (DCFH) is proposed for FANETs. In DCFH, each cluster head calculates the period of hello messages in its cluster based on its velocity. Then, a fire hawk optimizer (FHO)-based dynamic clustering operation is carried out to determine the role of each UAV (cluster head (CH) or cluster member (CM)) in the network. To calculate the fitness value of each fire hawk, a fitness function is suggested based on four elements, namely the balance of energy consumption, the number of isolated clusters, the distribution of CHs, and the neighbor degree. To improve cluster stability, each CH manages the movement of its CMs and adjusts it based on its movement in the network. In the last phase, DCFH defines a greedy routing process to determine the next-hop node based on a score, which consists of distance between CHs, energy, and buffer capacity. Finally, DCFH is simulated using the network simulator version 2 (NS2), and its performance is compared with three methods, including the mobility-based weighted cluster routing scheme (MWCRSF), the dynamic clustering mechanism (DCM), and the Grey wolf optimization (GWO)-based clustering protocol. The simulation results show that DCFH well manages the number of clusters in the network. It improves the cluster construction time (about 55.51%), cluster lifetime (approximately 11.13%), energy consumption (about 15.16%), network lifetime (about 2.6%), throughput (approximately 3.9%), packet delivery rate (about 0.61%), and delay (approximately 14.29%). However, its overhead is approximately 8.72% more than MWCRSF.

The comprehensive research on the transcritical CO2 thermal management system used in thermostatic transport vehicles

Natural fluid CO2 has become the preferred target of refrigerant alternative in the electrified transportation field in recent years to replace positive temperature coefficient (PTC) heaters, reduce energy consumption, and respect the international ban on Hydrofluorocarbons (HFCs) refrigerants. Thermostatic transport vehicles (TTVs) are well suited to exploit the benefits of CO2 due to their working conditions with a broad range of ambient temperatures, extensive transit distances, and substantial heating capacity requirements in cold climates. In this paper, a transcritical CO2 heat pump air-conditioning system used in TTV was constructed initially with proper control logic. Then following the assessment of the system’s steady-state performance, the dynamic operation process under high thermal inertia was thoroughly examined and analyzed in detail. Ultimately, the optimization of the dynamic response process was realized under varying heat flow boundary conditions. The primary results demonstrated that the rationally designed CO2 thermal management system performed admirably in various operating conditions, particularly in the heating mode where the coefficient of performance (COP) exceeded 3.2. Addressing the high-frequency fluctuation caused by the cargo thermal inertia, the temperature could be maintained with only two starts and stops within 3×105 seconds when the system control logic was refined. The optimized thermal management system of TTV efficiently allowed cargo to achieve the proper temperature as quickly as possible and sustain stability. This study provides an effective theoretical basis for the promotion of the transcritical CO2 thermal management system in the field of TTVs.

Reliability analysis for degradation process with abrupt jumps caused by operation state transition

This paper proposes a novel type of degradation process incorporating continuous degradation and abrupt degradation jump under dynamic operation states. By analyzing the failure mechanism of practical application, the abrupt degradation jump is comprehensively considered, and it is triggered by some certain state switching processes. Then, the reliability solutions of the degradation process (reliability function and moments of lifetime) are derived in the form of Laplace-Stieltjes transform. To a further step, inspired by the advanced equipment with long service span, the reliability solutions in limit region are explored, where an effective calculation method regarding statistical characteristics of degradation amount during regeneration epoch is pioneeringly proposed. To verify the proposed solutions in the form of Laplace-Stieltjes transform and their limit case, the representative example, spool valve, is implemented, and the results show the effectiveness and the feasibility of the solutions.

Interface Engineering to Operate Reversible Protonic Ceramic Electrochemical Cells Below 500 °C

AbstractThe low‐temperature (<500 °C) operation of reversible protonic ceramic electrochemical cells (PCECs) is desirable in achieving efficient and sustainable electricity generation, as well as green hydrogen production. However, significant interfacial resistance, which contributes to both ohmic and polarization resistance, remains a hurdle in lowering the operating temperature. In this study, PrBa0.5Sr0.5Co1.5Fe0.5O5+δ (PBSCF) and BaZr0.4Ce0.4Y0.1Yb0.1O3‐δ (BZCYYb) mono‐grain composite interlayers are introduced, which significantly extend the electrode/electrolyte interface and increase the concentration of vertically aligned oxygen vacancies along the heterointerface. This unique design achieves the lowest ohmic and polarization resistances among previously reported values in solid electrolyte‐based electrochemical cells. As a result, the PCEC can operate at extremely low temperature of 350 °C with an exceptional peak power density of 0.50 W cm−2 in fuel cell mode and current density of 0.25 A cm−2 at 1.3 V in electrolysis cell mode. Furthermore, it demonstrates high energy conversion efficiency and excellent stability under static and dynamic operating conditions.

A comparative study on the performance of Gray Wolves Optimization and Multi-Free Dynamic Schema

In this paper, we present a comparison study on the performance of Gray Wolves Optimization (GWO) and Multi-free dynamic schema (MFDS) algorithms. The Multi-free Dynamic Schema (MFDS) algorithm is a sophisticated optimization method created for solving optimization problems. It contains different operators (dynamic schema operator, dissimilarity operator, similarity operator and free dynamic schema operator). Where, The Grey Wolf Optimization (GWO) algorithm is a metaheuristic optimization algorithm inspired by the social behavior of grey wolves in a pack. This study focused on the run time and the number of iterations to reach the optimal solutions. The sample of this comparison was on ten functions. The results showed the superiority of an algorithm (MFDS) on (GWO) algorithm in most test functions, especially at the run time.

Revealing the critical role of low voltage excursions in enhancing PEM fuel cell catalyst degradation by automotive hydrogen/air potential cycling experiments

The durability of Polymer Electrolyte Membrane Fuel Cells under dynamic operation still needs to be improved. To understand the automotive voltage cycling-induced catalyst degradation, the loss of the electrochemically active surface area (ECSA) is investigated through an experimental campaign on Membrane Electrode Assemblies. Ad-hoc hydrogen/air accelerated protocols were designed to evaluate the voltage profile impact in a range relevant for both automotive and heavy duty transport application (<0.90 V). Besides the well-known aging dependence on the upper potential limit, this work evidences the critical role of the short-stops, characterized by low voltage transients. Effort was spent in studying this procedure parameters (voltage level, duration, scan rate, humidification). The accelerated ECSA loss is due to Pt nanoparticles coarsening as proved by transmission electron microscopy and is suspected dominated by Pt cathodic dissolution, incentivized during excursions towards very low potentials (<0.4 V). These findings help the development of system mitigation strategies.

Architecture for low-cost and highly flexible metro-access networks using SOA-based OADM nodes and digital subcarrier multiplexing with power loading.

Metro-access networks exploiting wavelength division multiplexing (WDM) to cope with the ever-growing bandwidth demands are sensitive to cost and need to be fast-configurable to meet the requirements of many new network services. Optical add-drop multiplexers (OADMs) are a key component in enabling fast dynamic wavelength allocation and optimization. In this Letter, we propose and demonstrate, to our knowledge, a novel architecture for high-performance metro-access networks that utilizes semiconductor optical amplifier (SOA)-based OADM nodes, digital subcarrier multiplexing (DSCM), low-cost direct detection receivers, and power loading techniques, which makes the designed metro-access network cost-effective, fast reconfigurable, and flexible for bandwidth allocation on demand. Through a proof-of-concept experiment, we have successfully demonstrated a prototype horseshoe optical network consisting of up to four SOA-based OADM nodes at 40 Gb/s per wavelength channel by leveraging the proposed scheme. The flexible bandwidth allocation and dynamic add and drop operations have also been achieved in an emulated WDM optical network. All results indicate the great scalability and flexibility of the proposed architecture.

Moderating effect of dynamic capabilities on the relationship between lean practices and operational performance

PurposeThe view that dynamic capabilities theory can help explain how lean organizations improve has been put forward by scholars. However, there is still a lack of research on the matching relationship between the application of lean practice and the internal elements of enterprise organization from the perspective of dynamic capabilities. The purpose of this study is to validate the moderating effect of dynamic capabilities on the relationship between lean practices and operational performance.Design/methodology/approachThis study used the method of survey and empirical research to collect sample data from 263 enterprises in China. Through literature review, this study put forward the moderating hypotheses around dynamic capabilities, lean practices and operation performance and used the method of regression analysis to validate these hypotheses.FindingsThe results showed that dynamic capabilities have a partially moderating effect on the application of lean practices. Specifically, dynamic capabilities have a significant moderating effect on the relationship between just-in-time, total quality management, total preventive maintenance and operational performance, while dynamic capabilities have no significant moderating effect on the relationship between human resource management and operational performance.Originality/valueThe research conclusion complements and enriches the lean practices literature from the perspective of dynamic capabilities. Existing studies mainly focus on the moderating role of external environmental factors, while there is a lack of empirical research on the role of dynamic capabilities in lean practices literature. The research results will help enterprises further understand the matching relationship between lean practices and dynamic capabilities and then improve the success of lean practices application.

Shipboard crane digital twin: An empirical study on R/V Gunnerus

The maritime industry is constantly facing new challenges and technological advancements, making the adaptation of innovative solutions like digital twins a bridge enhancing safety and efficiency between theoretical models and real-world maritime applications. Digital twin simulators for complex dynamic operation systems such as shipboard cranes, allow the operators to practice demanding operations in a risk-free immersive environment with various scenarios. This paper presents the process of developing a digital twin-based simulation tool designed for shipboard crane operations. It is based on co-simulation using the Functional Mock-up Interface (FMI) standard. This allows for the integration of the operation scenarios with different physical domain models, including such as the ship, environmental forces, hydraulic power systems, and controllers. In addition, the digital twin has opened up new opportunities for the optimization of maritime systems. It can be used to identify the optimal operating conditions, predict potential risks, and provide onboard support to the operator. A demo crane digital twin is implemented for the Palfinger crane on the Gunnerus research vessel with system validation via a series of marine operations being real-time monitored by the Remote Control Center (RCC).

State-of-Health Estimation for Sustainable Electric Vehicle Batteries Using Temporal-Enhanced Self-Attention Graph Neural Networks

Abstract Electric vehicles (EVs) have emerged as an environmentally friendly alternative to conventional fuel vehicles. Lithium-ion batteries are the major energy source for EVs, but they degrade under dynamic operating conditions. Accurate estimation of battery state of health is important for sustainability as it quantifies battery condition, influences reuse possibilities, and helps alleviate capacity degradation, which finally impacts battery lifespan and energy efficiency. In this paper, a self-attention graph neural network combined with long short-term memory (LSTM) is introduced by focusing on using temporal and spatial dependencies in battery data. The LSTM layer utilizes a sliding window to extract temporal dependencies in the battery health factors. Two different approaches to the graph construction layer are subsequently developed: health factor-based and window-based graphs. Each approach emphasizes the interconnections between individual health factors and exploits temporal features in a deeper way, respectively. The self-attention mechanism is used to compute the adjacent weight matrix, which measures the strength of interactions between nodes in the graph. The impact of the two graph structures on the model performance is discussed. The model accuracy and computational cost of the proposed model are compared with the individual LSTM and gated recurrent unit (GRU) models.

Predicting the dynamic behavior of a magnetocaloric cooling prototype via artificial neural networks

Although magnetocaloric cooling is considered a promising long-term alternative to vapor compression, recent prototype developments have not yet made this technology commercially competitive, primarily due to its high energy consumption and lack of cost-effective, long-term mechanically-chemically stable materials. To address the first issue and understand how the efficiency of magnetocaloric systems can be improved, dynamic models can offer valuable insights into their transient operation. This work focuses on the development of an artificial neural network with experimental data to model the dynamic operation of a magnetic refrigeration system. Through a design of experiments approach, we propose excitation signals for the identification experiment, involving five manipulated variables and one selected disturbance as inputs, with the output temperature of the cold manifold and power consumption as the target parameters. We chose a nonlinear autoregressive artificial neural network with exogenous inputs to model the transient operation of the system. The temperature model achieved R2 values of 0.995 and 0.955 for the 1-step and 90-step ahead predictions, respectively. Similarly, the power consumption model achieved R2 values of 0.988 and 0.949 for the 1-step and 90-step ahead predictions, respectively. These performance metrics were evaluated on the test sets that were not used for training the models, highlighting the robustness and accuracy of the models in both short-term and long-term predictions.

Transient simulation and experimental analysis for the response of dual-cycle air-carrying energy radiant diffuse terminal

A novel air-carrying energy radiant diffuse terminal is proposed, and the response of the terminal is investigated by field experiment and numerical simulation. This paper explores the dynamic operation characteristics of the new system to guide the optimal operation of the system. A transient simulation method of three factors (conduction, convection, and radiation) with dual-circulation (air-carrying energy zone and occupied zone) is put forward for the radiant diffuse terminal with a fresh air system. This transient simulation sets up the porous baffle model coupling radiation model concurrently, revealing the dynamic thermal performance for the system's response accurately, quickly, and comprehensively. This method avoids the limits for the complexity of mini porous (Pore size of 1 mm–3mm) geometric models and the transient uncertainty in multiple combination models. The results show that the average error between the convective-conductive model and the convective-conductive-radiative model is about 8% at five different heights. This system has high response performance, which can quickly heat the room while maintaining the comfort requirements of vertical temperature. The radiated heat transfer between the radiant diffuse terminal and the occupied zone is greater than the convective heat transfer (Radiation accounts for 74%). This system has a short response time, high energy efficiency, uniform airflow, and eliminates draught rating. Moreover, the indoor non-uniform transient thermal and humid environments, the thermal comfort, and air quality for the response of the terminal with fresh air system under four different diffuser placements are compared using computational fluid dynamic simulation. The results indicate that introducing heating strategies featuring higher-positioned fresh air inlet and lower-positioned recirculation return air outlet for this system leads to a temperature efficiency increase by 5%, velocity uniformity by 8%, and age of air reduction by 16–50% in human-breathed zone and the response time by 25%, which shows substantial potential for energy savings. The transient simulation and experiments in the paper are beneficial to optimize design parameters and carry out energy-saving applications of operation energy consumption for the response of this terminal with a fresh air system in the future.

Three-phase voltage sensitivity estimation and its application to topology identification in low-voltage distribution networks

This paper aims to estimate the three-phase voltage sensitivity matrix and the network topology of a low-voltage distribution network from smart meter data, which measures voltage magnitude, current magnitude, and power factor with a lead/lag indicator. The targeted networks are three-phase networks with single-phase loads. Understanding network sensitivity and topology is crucial for fault detection, unmetered load identification, and addressing Dynamic Operating Envelope problems. The problem is formulated as a constrained optimization problem to estimate both the three-phase voltage sensitivity and the low-voltage transformer voltage. The estimated voltage sensitivity is used to further identify the network topology, by implementing an enhanced Recursive-Grouping and Backtracking algorithm, as well as a candidate topology selection technique. The proposed method is tested on the 55-node European feeder and several synthetic networks. Compared to the state-of-the-art, the results show a four-fold improvement in the accuracy of voltage sensitivity estimation and substantially fewer mistakes in the topology estimates. The result underscores the efficacy of a three-phase network model and voltage angle approximation in enhancing estimation accuracy.

A preliminary investigation on enabling digital twin technology for operations and maintenance of urban underground infrastructure

Underground infrastructure plays a kind of crucial role in modern production and living, especially in big cities where the ground space has been fully utilized. In the context of recent advancements in digital technology, the demand for the application of digital twin technology in underground infrastructure has become increasingly urgent as well. However, the interaction and co-integration between underground engineering entities and virtual models remain relatively limited, primarily due to the unique nature of underground engineering data and the constraints imposed by the development of information technology. This research focuses on underground engineering infrastructure and provides an overview of the application of novel information technologies. Furthermore, a comprehensive framework for digital twin implementation, which encompasses five dimensions and combines emerging technologies, has been proposed. It thereby expands the horizons of the intersection between underground engineering and digital twins. Additionally, a practical project in Wenzhou serves as a case study, where a comprehensive database covering the project’s entire life cycle has been established. The physical model is visualized, endowed with functional implications and data analysis capabilities, and integrated with the visualization platform to enable dynamic operation and maintenance management of the project.

What Do Hebbian Learners Learn? Reduction Axioms for Iterated Hebbian Learning

This paper is a contribution to neural network semantics, a foundational framework for neuro-symbolic AI. The key insight of this theory is that logical operators can be mapped to operators on neural network states. In this paper, we do this for a neural network learning operator. We map a dynamic operator [φ] to iterated Hebbian learning, a simple learning policy that updates a neural network by repeatedly applying Hebb's learning rule until the net reaches a fixed-point. Our main result is that we can "translate away" [φ]-formulas via reduction axioms. This means that completeness for the logic of iterated Hebbian learning follows from completeness of the base logic. These reduction axioms also provide (1) a human-interpretable description of iterated Hebbian learning as a kind of plausibility upgrade, and (2) an approach to building neural networks with guarantees on what they can learn.

Dynamic control strategy of the ternary azeotrope separation process assisted by reactive-extractive distillation for ethyl acetate/isopropanol/water

The double-column reactive-extractive distillation (DCRED) process is a promisingly intensified compactness configuration for separating ternary water-containing azeotrope(s) by introducing the external reactions, such as the typical common adopted epoxy ethane (EO) hydration reaction. However, limited investigation has focused on the dynamic control analysis for the highly strong coupled DCRED process. In this paper, the efficient and robust control structure is proposed to achieve the dynamic operation of the DCRED process for the system of ethyl acetate/isopropanol/water. The composition controller is applied to precisely control the feed ratio of EO and water, thereby ensuring the qualified products when facing ± 10 % feed flowrate disturbances and ± 5 % feed composition disturbances. This work provides an alternative control strategy for the DCRED process, which is beneficial for its practical industrial applications.

Evaluation of Thermomechanical Properties in a 2D Rotational Elastocaloric Prototype: A Numerical Study for Enhanced Energy Efficiency

So far, much of the research on the caloric effect has focused on the magnetocaloric effect, which was the first investigated chronologically, in the field of room temperature for about 40 years. Subsequently and especially in the last decade, scientific research has focused on the development of solid-state technologies other than the magnetocaloric one, including the one of interest for this work: elastocaloric technology. This work is part of the “SUSSTAINEBLE” project of the Department of Industrial Engineering at the University Federico II of Naples, aimed at developing the first Italian prototype of an elastocaloric device for environmental conditioning. The prototype is currently in the experimental development phase and its design and construction are dynamically accompanied by a two-dimensional numerical model that fully reproduces its thermo-fluid dynamic operation. The rotary-type prototype consists of 600 Nickel Titanium wires subjected to loading and unloading phases controlled by a properly programmed optical encoder. The thermo-fluidic medium that regulates heat transfer is air. The aim is to characterize the operation of the elastocaloric device using numerical analysis software in order to optimize its geometric, operational, and environmental parameters, to maximize its energy performance in terms of temperature difference, useful thermal power, and coefficient of performance.

Thermal Management of Proton‐Exchange Membrane Fuel Cells with Enhanced Fuzzy Control via an Improved Whale Optimization Algorithm

Proton exchange membrane fuel cells (PEMFCs) require an appropriate operating temperature to achieve high performance and longevity. In addressing the critical need for effective thermal management in PEMFCs for vehicle propulsion systems, this study has developed and introduced a novel thermal management system model equipped with dual‐cooling loops. Addressing the issues of slow response and poor dynamic performance inherent in traditional control strategies, as well as the computational complexity that hinders the practical application of existing advanced control strategies, this research proposes a fuzzy proportional‐integral‐derivative (PID) controller optimizing the improved whale optimization algorithm (IWOA‐FPID) aimed at enhancing fan side control. By simulating step changes in current load and designing dynamic operating conditions based on the most basic and simple energy management system, the performance of IWOA‐FPID in temperature control is validated. The final results indicate that the IWOA‐FPID strategy significantly outperforms existing control methods, notably reducing adjustment time and minimizing temperature overshoot to ≈0.5 K. The proposed framework achieves efficient temperature control, highlighting its potential for broader application in vehicular fuel cells and offering a promising solution to the challenges of PEMFC thermal management.

Ultra-broadband tunable terahertz metasurface absorber with multi-mode regulation based on artificial neural network

The multi-window tunable absorption of terahertz (THz) waves faces challenges such as a small tunable bandwidth, discontinuous tuning windows, and a complex design process. This study addresses these issues by employing inverse design based on artificial neural networks (ANN) to achieve an ultra-broadband tunable THz metasurface absorber (UTTMA). Leveraging the electrically tunable property of graphene and the phase transition property of vanadium dioxide (VO2), the UTTMA achieves multi-domain dynamic operation across a large frequency band. The design allows for arbitrary switching between multiple windows by transforming between graphene surface-localized plasmon resonance (LSPR), fundamental order surface plasmon polaritons (SPP) resonance, and graphene surface plasmon resonance (SPR), and second order SPP resonance. The tunable windows cover the range between 1.90 and 10.00 THz, encompassing four-fifths of the entire THz band, surpassing previously reported absorbers. The accuracy of the ANN inverse design is verified, and a systematic analysis of the optical performance of the UTTMA is conducted. These findings offer a viable and effective method for expanding the tunable bandwidth and simplifying the design process for THz metasurfaces.