Changes In Operating Conditions Research Articles

Stability analysis of axial compressors with wire mesh casing treatment

Experimental results indicate that wire mesh casing treatment (WMCT) enhances the stability of low-speed compressors with little reduction in efficiency. The flow resistance on the surface of the grooves, which are formed by the wire mesh, provides a new variable for the design of WMCT. This paper investigates the stability improvement induced by three different meshes through experiments and steady numerical simulations. A stability prediction model incorporating the effect of WMCT is developed to assess the stability of the steady flow field, and the results show that the predicted stall points of the compressor are close to those of the experimental data. The stability analysis model has a well-defined theoretical foundation in which the meridional flow field of the compressor is considered as the basic flow and the blade is replaced with a body force. This formulation enables fast and accurate stability assessments of compressors incorporating WMCT during the design process. Finally, based on the influence of the different meshes on the steady flow field, the stability-enhancing mechanism of WMCT is analyzed in terms of flow field details and macroscopic physical quantities. WMCT improves the flow around the tip region, shifting the tip blade loading in the aft direction and reducing the tip leakage flow. Macroscopically, the installation of WMCT makes the flow structure in the tip region less sensitive to changes in the compressor operating conditions.

Rotary Kiln Temperature Control Under Multiple Operating Conditions: an Error-Triggered Adaptive Model Predictive Control Solution

The temperature of rotary kiln, as one of the essential equipment in zinc smelting process, determines the product quality and resource utilization. However, since rotary kiln is a large-scale and highly coupled system, there are plenty of variables affecting the rotary kiln temperature, bringing too much redundant information to describe rotary kiln dynamics. In addition, due to the variations in raw materials, production load, and market demand, there exist various operation conditions, making it difficult to achieve stability control of the rotary kiln temperature with traditional control methods. To solve these problems, an error-triggered adaptive model predictive control (ET-AMPC) is proposed in this article. Specifically, since rotary kiln temperature is hard to regulate due to redundancy among variables and strong nonlinearity, an orthogonal maximum mutual information coefficient feature selection (OMICFS) method is first proposed to determine vital variables affecting temperature most. Then, aiming at the problem of changing operating conditions of rotary kilns, an ET-AMPC method is proposed, which can precisely adapt to different operating conditions and achieve stability control. Finally, experiments on a numerical simulation case and an industrial rotary kiln show the strength and reliability of the proposed method, which reduces 10%–20% trajectory tracking error in the period of operating condition changing and improves the control accuracy effectively.

Valve Setting Using Fuzzy Logic Method To Control Main Engine Coolant Flow

The main objective of this research is to improve the operating efficiency and performance of ship main engines by optimizing coolant flow. Improper coolant flow can cause excessive temperature rise in the engine, which in turn can reduce engine life and efficiency. In this research, the coolant flow control system is implemented using the fuzzy logic method. Fuzzy logic allows modeling that is more adaptive to parameter variations and uncertainties in the operational environment. Fuzzy rules are developed based on practical knowledge of experts and operational data related to the host machine. The test was carried out through a simulation using a water flow control circuit that supplies the coolant to the ship's main engine with the main components being an ESP32 microcontroller chip and three DS18B20 temperature sensors. The results of this research show that the use of fuzzy logic in regulating coolant flow is able to provide a faster and more accurate response to changes in engine operational conditions. This has the potential to increase cooling efficiency and prevent engine over-temperatures, which can ultimately increase the overall service life and performance of the host engine. The practical implications of the results of this research can be applied in the development of more intelligent and adaptive control systems for various types of host machines, with the potential to increase operating efficiency and reduce the risk of damage due to excessive temperatures

Automatic trajectory adaptation for the control of quality characteristics in a powder compaction process

The manufacturing process of sintered components requires the compaction of powder in a rigid die and the sintering of the resulting green parts. To produce green parts of the same constant quality over a long period of time, regular quality checks and trajectory adjustments are needed to account for changing operating conditions due to e.g. varying temperature, humidity, stroke rate, or powder quality. Also the first setup of the machine requires manual measurements and iterative adjustments of trajectory key points. To overcome these labour-intensive, manual measurements and adjustments, we propose an algorithm for the automatic adaptation of trajectory key-points that for the first time allows bringing and keeping more than one quality characteristic (mass and dimensions) fully automatically within predefined intervals. To safe costs, we further present two real-time capable, model-based solutions by employing grey-box and black-box models for the online estimation of the workpiece mass and height instead of their direct measurement. The grey-box model combines a filling and analytical compaction model and allows estimating mass and height rather than density as typically investigated in literature. The black-box model based on neural networks represents a complete novelty in the field as no similar data-driven approach could be found that aims at estimating mass and dimensions of green compacts. We finally compare the performance of these models to the baseline of sensor measurements as well as study their effect on the overall control performance. Results indicate that for both sensor-based and model-based solutions it is possible to reach and keep aforementioned quality characteristics within desired intervals. While overall the sensor-based approach performed best, also for the model-based approaches a good estimation performance is observed with the black-box model based on neural networks outperforming the grey-box model. While the black-box approach is fully data-driven and thus allows to widely avoid manual modelling activities, it still requires the presence of expensive sensors for capturing training data. The proposed grey-box approach, on the other hand, requires some manual modelling and model calibration activities, but provides a cost-effective solution to the problem and represents an alternative to more expensive, sensor-based implementations.

The investigation of a fuzzy-internal mode PID-based temperature control system for solid state electric storage heaters

It is a fuzzy internal mode PID temperature control system designed for solid electric storage heating systems with high inertia, hysteresis, and nonlinearity. Experimental results demonstrate that the control strategy has a high level of model adaptability and anti-interference capability for the changing operating conditions of solid-storage boilers, that the system overshoot is steadily controlled within 5% and that hysteresis cannot adversely affect the stability of the control system, that the control accuracy of water supply temperature can reach ± 1∘C, and that the control quality is superior to traditional PID strategies.

An Adaptive Activation Transfer Learning Approach for Fault Diagnosis

For industrial scenarios with changing operating conditions, the vibration data of different operating conditions often have different data distributions. In this article, to make the deep learning framework perform more flexible nonlinear transformations for different input data, a new activation function, i.e., parameter-free adaptively Swish (PASwish), is developed. PASwish formulates different activation schemes for different input data so that vibration data under different operating conditions can carry out adaptive nonlinear transformation, and the generalization ability of the whole network is improved. In addition, this article proposes deep parameter-free cosine networks with PASwish on the basis of PASwish, which can help adjust the network weights of domain-specific features and domain-invariant features by constructing an attention module based on cosine adjustment. Finally, the reconstruction-based domain adaptation method is used to achieve cross-domain fault diagnosis. Experiments are carried out on the bearing fault experimental platform to verify the effectiveness and generalization of the proposed method. We have achieved 95.16 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ \pm 1.76\%$</tex-math></inline-formula> ) average accuracy on 72 transfer tasks, which shows better performance than current studies.

Flexible operation of Post-combustion CO2 capture process enabled by NARX-MPC using neural network

Post-combustion CO2 capture (PCC) process is expected to serve as the role of a bridge solution during the transition from fossil-based to renewable energy, but extensive energy consumption is a significant hurdle to its widespread commercial adoption. Operating a PCC process economically often requires large operating condition changes in response to power plant load changes and time-varying energy prices. The ‘flexible’ operation requires a controller that aids in such a dynamic operating environment of the PCC process. To balance model complexity with prediction accuracy, we adopt NARX-MPC which employs a neural network-based NARX model as a prediction model for MPC. Under various dynamic operation scenarios, the proposed NARX-MPC is evaluated and compared with a linear MPC (LMPC) controller. Our observations indeed reveal that the NARX-MPC delivers significantly superior closed-loop control performance over the LMPC, especially in scenarios involving large changes in the power plant load or inlet flue gas flow rate and simultaneous changes in the flue gas flow rate and the CO2 capture rate.

A theoretical study on altering the frequency of high-order modes using a time-varying wall property

Noise radiation by aero-engines features high circumferential modes and high frequencies. It is rather difficult to suppress such high-order modes near their cut-on frequencies. Delicate designs are needed. When the engine operation conditions change, a design good for one set of parameters may not be optimal for another. This study explores a new path: an electromagnetic diaphragm with a MOSFET-controlled shunt circuit can be used to convert the wave frequency to any value at will by creating a digitally pre-defined time-varying material property. When properly organized, the incident sound energy is dispersed to several frequencies, some of which are below the cutoff frequency. In this way, it is showed that part of the energy of high-order modes can be reduced. This method creatively utilizes electroacoustic coupling to alter frequencies and control the propagation of high-order modes. Additionally, the time-variation is made on a passive basis in the sense that it requires no sensor input and no energy input to the actuator, thus ensuring stability and potential application in engineering.

Multi-condition operational optimization with adaptive knowledge transfer for municipal solid waste incineration process

To satisfy incineration efficiency and nitrogen oxides ultra-low emission requirements, it is of great importance to improve the operational performance of the municipal solid waste incineration (MSWI) process. However, due to the complex and fluctuating composition of municipal solid waste, it is challenging to guarantee optimal operation under changing conditions. To address this issue, a multi-condition operational optimization method based on adaptive knowledge transfer (MCOO-TR) is proposed for the MSWI process. First, an integrated optimization scheme, where different operation conditions are identified based on cluster analysis and expert knowledge, is developed. Second, a surrogate-assisted multi-objective particle swarm optimization algorithm is designed to solve the data-driven optimization problems for optimal operation, where the surrogate model selection mechanism is proposed to cope with multi-condition characteristics. Third, an adaptive knowledge transfer strategy, corresponding to condition identification, is designed aiming to improve optimization efficiency. Finally, the proposed MCOO-TR method is evaluated based on actual industrial data. The experimental results demonstrate that the MCOO-TR method can achieve satisfactory operational performance under changing operation conditions.

Quantifying the Resistive Losses of the Catalytic Layers in Anion-Exchange Membrane Fuel Cells.

The existing gap in the ability to quantify the impacts of resistive losses on the performance of anion-exchange membrane fuel cells (AEMFCs) during the lifetime of their operation is a serious concern for the technology. In this paper, we analyzed the ohmic region of an operating AEMFC fed with pure oxygen followed by CO2 -free air at various operating currents, using a combination of electrochemical impedance spectroscopy (EIS) and a novel technique called impedance spectroscopy genetic programming (ISGP). Presented here for the first time in this work, we isolated and quantified the individual effective resistance (Reff ) values occurring in the AEMFC and their influence on performance as operating conditions change. We believe that this first work is vital to help distinguish the influence of the individual catalytic and mass-transfer processes in this technology thereby providing valuable data to the AEMFC community, with potentially wider applicability to other electrochemical devices where individual physical processes occur simultaneously and need to be sequestered for deeper understanding.

Observation and mitigation of perimeter artefacts in bench scale membrane characterization

Perimeter artefacts, such as test cell induced membrane damage and unrepresentative hydrodynamics, can significantly affect bench scale performance evaluation using membrane coupons. By comparing separately collected center and perimeter permeate, the effect of perimeter artefacts can be identified. The additional salt passage in the perimeter area (i.e., along the edges of the membrane coupon) depends non-linearly on pressure, and is implicitly affected by flux, temperature and salinity (osmotic pressure), leading to a potential increase of the measured salt passage by a factor two. We observed a very slow dynamic response (τ≈12h) to changes in operational conditions, suggesting that in our case the perimeter artefact was diffusive in nature. Due to this slow response, the perimeter salt passage trails previously evaluated operational conditions, possibly resulting in a false trend, hysteresis or high variance, depending on the sequential ordering and duration of test condition intervals. The effect of perimeter artefacts can be largely mitigated by measuring salt rejection from only the central area of the coupon.

An Adaptive Inertia and Damping Control Strategy Based on Enhanced Virtual Synchronous Generator Model

In modern converter-dominated power systems, total inertia is very variable and depends on the share of power generated by renewable-based converter-interfaced generation (CIG) at each specific moment. As a result, the limits required by the grid codes on the rate of change of frequency and its nadir or zenith during disturbances become challenging to achieve with conventional control approaches. Therefore, the transition to a novel control strategy of CIG with a grid-forming power converter is relevant. For this purpose, a control algorithm based on a virtual synchronous generator (VSG) is used, which simulates the properties and capabilities of a conventional synchronous generation. However, due to continuously changing operating conditions in converter-dominated power systems, the virtual inertia formed by VSG must be adaptive. At the same time, the efficiency of adaptive algorithms strongly depends on the used VSG structure. In this connection, this paper proposes an enhanced VSG structure for which the transfer function of the active power control loop was formed. With the help of it, the advantages over the conventional VSG structure were proven, which are necessary for the effective adaptive control of the VSG parameters. Then, the analysis of the impact of the VSG parameters on the dynamic response using the transient characteristics in the time domain was performed. Based on the results obtained, adaptive algorithms for independent control of the virtual inertia and the parameters of the VSG damper winding were developed. The performed mathematical modeling confirmed the reliable and effective operation of the developed adaptive control algorithms and the enhanced VSG structure. The theoretical and experimental results obtained in this paper indicate the need for simultaneous development and improvement of both adaptive control algorithms and VSG structures used for this purpose.

Resonance diagnostics of production space of generative systems of artificial intelligence

The development of artificial intelligence generative systems (AIGS) in the modern world requires addressing issues related to the quality, stability, and efficiency of the generated content. In this context, resonance diagnostics become of paramount importance. The purpose of this study is to explore the possibilities of applying resonance diagnostics for detecting, analyzing, and resolving problems in artificial intelligence generative systems. To achieve the set goal, the following tasks were identified: analysis of the theoretical foundations of resonance diagnostics; investigation of the potential of using resonance signals to adjust AIGS learning parameters; studying the impact of resonance diagnostics on the stability and adaptation of AIGS to changing operating conditions. The study conducted an analysis of resonance diagnostics in the context of AIGS and revealed its powerful influence on addressing issues related to system quality and productivity. The research demonstrated that resonance diagnostics can be used to achieve realism, diversity, and quality of generated content. Additionally, it was determined that it can contribute to enhancing the stability and adaptation of systems to varying operational conditions

Load Carrying Capacity Enhancing Design and Lubrication Investigation of the Magnetic-Water Double Suspension Elastic Support Thrust Bearing

Aiming at the problem that the traditional water-lubricated bearing cannot carry the heavy load and adapt to the constantly changing operating conditions for the high-power Rim Driven Thruster (RDT), the principle structure of the Magnetic Water-double-suspension Elastic-support Thrust Bearing (MWETB) is designed and the optimal structure parameters of the bearing are selected using simulation. To demonstrate the reliability of the MWETB under the RDTs’ actual working conditions, performance tests, which include the magnetic flux density, magnetic force, and lubrication performance, are carried out. The simulation and experimental results indicate that the optimal offset ratios are in two intervals, and the magnetic alignment and sheath materials have a great effect on the load reduction. The load-carrying force has obvious zoning characteristics with the change in bearing clearance. Besides, compared with the water-lubricated thrust bearings, the MWETB has advantages in terms of minimum film thickness and friction coefficient.

Adaptive machine learning based control of multi-temperature, multi-module latent thermal storage ensembles

Phase change material thermal energy storage systems can be used to alleviate disparities in renewable energy supply and demand. Performance of such systems can be enhanced using a cascade of multiple phase change materials but efficiency benefits currently rely on precise optimization of design parameters. Therefore, there is motivation to develop storage technologies which provide greater flexibility for applications with dynamic operating conditions. In this study, a novel system is proposed called a multi-temperature, multi-module ensemble which consists of multiple phase change materials combined in series and parallel configurations. The system is optimized and controlled by an artificial neural network trained using a multi-objective optimization scheme. The performance for the novel system is compared to two baseline systems: a single phase change material system and a cascaded system. Across a wide range of fixed operating conditions, the results show that the ensemble reduces the rate of exergy destruction by 60% compared to the single module system and achieves a 10 °C improvement in performance for matching target outlet temperature compared to the cascaded system. Over a dynamically changing load profile, the results show that the multi-temperature, multi-module ensemble with artificial neural network controller is the preferable design as it achieves the excellent exergy efficiency of a cascaded system while maintaining the flexibility needed to respond to dynamically changing operating conditions. Furthermore, this novel approach widens the potential applications of latent thermal energy storage by providing a means to standardize manufacturing to drive down system cost and allowing the system to adapt to unforeseen design parameters.

A sequential cross-product knowledge accumulation, extraction and transfer framework for machine learning-based production process modelling

ABSTRACT Machine learning is a promising method to model production processes and predict product quality. It is challenging to accurately model complex systems due to data scarcity, as mass customisation leads to various high-variety low-volume products. This study conceptualised knowledge accumulation, extraction, and transfer (KAET) to exploit the knowledge embedded in similar entities to address data scarcity. A sequential cross-product KAET (SeqTrans) is proposed to conduct KAET, integrating data preparation and preprocessing, feature selection (FS), feature learning (FL), and transfer learning (TL). The FS and FL modules conduct knowledge extraction and help address various practical challenges such as changing operating conditions and unbalanced datasets. In this paper, sequential TL is introduced to production modelling to conduct knowledge transfer among multiple entities. The first case study of auxetic material performance prediction demonstrates the effectiveness of sequential TL. Compared with conventional TL, sequential TL can achieve the same test mean square errors with 300 fewer training examples when facing data scarcity. In the second case study, balancing anomaly detection models were constructed for two gas turbines in the same series using real-world production data. With SeqTrans, the F1-score of the anomaly detection model of the data-poor engine was improved from 0.769 to 0.909.

Improvement in Identification Accuracy of a Failure Diagnostic System for a Reusable Rocket Engine

As a technology for safe and efficient operation of reusable rockets, we are developing failure diagnosis technology for reusable rocket engines. In order to follow the changes in rocket engine operating conditions, a failure diagnostic method which monitors an error vector: the difference between the predicted and measured values of the sensors was developed. The method contains anomaly detection by Mahalanobis distance and failure identification by support vector machines (SVMs). In this report, combinations of monitoring sensors of SVMs for each failure mode were optimized by using design of experiments. By using optimal sensor combinations, the F-score of SVMs were improved in all failure modes. From the results of the orthogonal table experiments, it was supposed that sensors which show the difference in failure modes are important to distinguish failure modes. In addition, a failure classifier combined with the optimized SVMs for each failure mode was developed and demonstrated. The performance of the combined failure classifier with the optimal sensor combination was mostly greater than that with all sensors. However, degradation of the classification performance was also obtained. It is necessary to consider how integrate the results of SVMs which are optimized individually.

A Novel Unsupervised Clustering and Domain Adaptation Framework for Rotating Machinery Fault Diagnosis

Deep learning-based fault diagnosis methods require a large number of labeled datasets. However, considering the changing operating conditions, it is impractical to obtain labeled datasets for all cases. Therefore, this study proposes a new unsupervised clustering and domain adaptation framework to circumvent data deficiency and domain issues. The proposed framework comprises two steps: unsupervised clustering and domain adaptation. In the unsupervised clustering, an expectation-maximization adversarial autoencoder, which combines an expectation-maximization algorithm with an adversarial autoencoder, is used for feature extraction and subspace mapping. Subsequently, the mapped features are clustered using a Gaussian mixture model. In the domain adaptation, a domain synchronization that is based on the symmetric Kullback-Leibler divergence metric is used to infer the relationship between the source and target domain clusters. The experiments on two rolling-element-bearing datasets validate the effectiveness of our method. Specifically, our method performs domain adaptation without retraining, which is promising for real industrial applications.

Study on one-dimensional performance prediction of multi-stage axial turbine based on the blade height

Abstract As turbine operating conditions change, the blade height and tip clearance undergo continuous alterations due to the combined effects of thermal stress, aerodynamic forces and centrifugal forces, subsequently influencing the turbine performance. To take this effect into account in turbine performance prediction, this study considers the influence of fluid-heat-structure coupling on blade height and tip clearance and establishes a one-dimensional comprehensive prediction method for multi-stage axial turbine performance considering blade height. When compared with experimental results from a four-stage axial turbine, by considering the fluid-thermal-solid coupling effects, the average relative error in total pressure ratio prediction is reduced from 3.76 % to 1.99 % and the average relative error in total temperature ratio prediction is reduced from 2.03 % to 1.26 %. Compared with the traditional flow prediction method, the prediction results of turbine characteristics considering blade height and tip clearance changes in this paper are closer to the experimental results.

The Interdependence of Environmental Activities and Investment Attractiveness: Finances of Russian Metallurgy

All countries now share a long-term vision of the importance of implementing technology development and transfer to improve climate resilience and reduce greenhouse gas emissions. Metallurgical enterprises play a significant role in achieving this goal, since they produce a large amount of carbon dioxide emissions into the atmosphere. In connection with the changing operating conditions and changing markets of presence, the issues of ensuring their investment attractiveness are acquiring obvious importance in the framework of the finances of Russian metallurgical companies. The object of the study is an assessment of the investment attractiveness of Russian metallurgical companies. The subject of the study is the relationship between the investment attractiveness of metallurgical companies and the results of their environmental protection activities. The purpose of this study is to identify the interdependence of environmental metrics and the investment attractiveness of steel companies. The methodological basis is a regression analysis of the impact of environmental metrics on the investment attractiveness of metallurgical companies. The authors chose the following indicators of environmental performance: CO2 emissions, energy consumption, water recycling, waste. To assess the investment attractiveness of metallurgical companies, the following indicators were used: revenue, EBITDA, investment in R&amp;D. The authors concluded that the environmental activities of companies have a significant impact on their investment attractiveness. The scientific novelty of the study lies in identifying the interdependence of environmental protection activities and the investment attractiveness of Russian metallurgy companies. The results of the study can be used by both Russian steel companies and institutional investors as part of the development of an investment strategy.