Parameter Identification Process Research Articles

An online battery–state of charge estimation method using the varying forgetting factor recursive least square - unscented Kalman filter algorithm on electric vehicles

Accurate and fast estimation of the state of charge is important for the battery management system of electric vehicles. This paper proposes a method to estimate the state of charge of Lithium-ion batteries by the <span lang="EN-US">variable</span> forgetting factor recursive least square (VFFRLS) – unscented Kalman filter (UKF) algorithm in real-time without the off-line battery testing data. Since the state observation requires an accurate model, an equivalent circuit model was constructed. Then, the VFFRLS algorithm is used to identify online the battery model parameters based on voltage and current measurements. An advantage of this algorithm is that it requires less initial information and shorter identification time than offline parameter identification. After the model parameters are well identified, the unscented Kalman filter estimates the state of charge and minimizes noise characteristics and uncertainty in the parameter identification process. The VFFRLS algorithm applied in this paper has shown a good result with the model output error of less than 1%, and the identification achieves real-time response. The state of charge obtained by the UKF algorithm has shown satisfactory estimation results with fast convergence speed and small errors. The UKF filter provides the results with a 1.5% error from the reference and converges after 10 cycles.

FePO4.2H2O recovery from acidic phosphate-rich waste streams

Phosphorous not only needs to be removed to prevent eutrophication of wastewater effluent receiving surface water bodies, but it also has to be recovered as a scarce finite reserve. Phosphorus chemical precipitation as NH4MgPO4·6H2O, Ca3(PO4)2, or Fe3(PO4)2 ·8H2O is the most common method of phosphorus recovery from phosphorus-rich streams. These minerals ideally form under neutral to alkaline pH conditions, making acidic streams problematic for their formation due to the need for pH adjustments. This study proposes FePO4 .2H2O (strengite-like compounds) recovery from acidic streams due to its simplicity and high efficiency, while also avoiding the need for pH-adjusting chemicals. The effect of initial pH, temperature, Fe (III) dosing rates, and Fe (II) dosage under different oxidation conditions (pO2 = 0.2, 1, 1.5 bar, different H2O2 dosing rates) on phosphorus recovery percentage and product settleability were evaluated in this study. The precipitates formed were analyzed using optical microscopy, SEM, XRD, SQUID, Raman, and ICP. Experiments showed that Fe (III) dosing achieved phosphorus recovery of over 95 % at an initial pH of 3 or higher, and the product exhibited poor settleability in all initial pH (1.5-5), and temperature (20–80 °C) tests. On the other hand, Fe (II) dosage instead of Fe (III) resulted in good product settleability but varying phosphorus recovery percentages depending on the oxidation conditions. The novelty of the study lies in revealing that the Fe (II) oxidation rate serves as a crucial process-design parameter, significantly enhancing product settleability without the requirement of carrier materials or crystallizers. The study proposes a novel strategy with controlled Fe2+-H2O2 dosing, identifying an Fe (II) oxidation rate of 4.7 × 10−4 mol/l/min as the optimal rate for achieving over 95 % total phosphorus recovery, along with excellent settleability with a volumetric index equal to only 8 ml/gP.

Implementation of inverse method for identification of process parameters in a laser heating process

Changes in process parameters can alter the temperature data of a solid body during the laser heating process. This paper proposes an inverse method to determine the laser heating parameters, namely, sheet width, sheet thickness, laser power, and scan speed of the work specimen for the given temperature response which is assigned by the user. The method uses an analytical tool on moving heat source as a forward model capable of real-time temperature prediction of the sheet under the laser heating process. The effectiveness of the present forward model that incorporated the temperature-dependent material properties is tested by experimental findings performed on Al 6061-T6 sheets. Next, an iterative search process based on heuristic method is carried out for satisfying a prescribed temperature response by the optimization of unknown parameters. To illustrate the implementation and assess the practicality of the inverse method, two examples based on laser heating applications are used. The method proposed herein recover the unknowns for the prescribed temperature response after a few iterations, provides an efficient way to optimize the laser heating process. Additionally, the effect of measurement error on the findings of the inverse problem is addressed.

Design and development of an LC compensated WPT system with multiple constant voltage outputs function

SummaryWith the increasing demand for the constant voltage (CV) charging of multiple loads, this article proposes an LC compensated wireless power transfer (WPT) system with multiple load‐independent CV outputs function. Each transmission channel consists of a compensation capacitor and a compensation inductor forming a resonant loop. By configuring different combinations of compensation inductor and capacitor for each transmission channel, different CV outputs can be achieved without affecting the output voltages of other transmission channels. In addition, the proposed system can achieve near zero phase angle (ZPA) and zero‐voltage switching (ZVS) operations, thereby improving system efficiency. Firstly, a detailed theoretical derivation of the proposed system with multiple load‐independent CV outputs and ZPA operation is performed, and the detailed parameter design process is provided. Secondly, the implementation method of ZVS operation is introduced and verified through simulation. Finally, an experimental prototype of two transmission channels is taken as an example to verify the correctness and rationality of the proposed system.

A thermodynamically consistent phase transformation model for multiphase alloys: application to Ti$$_6$$Al$$_4$$V in laser powder bed fusion processes

A thermodynamically consistent phase transformation model for multiphase alloys: application to Ti$$_6$$Al$$_4$$V in laser powder bed fusion processes

Fracture propagation morphology and parameter optimization design of pre-CO2 hybrid fracturing in shale oil reservoirs, Ordos Basin

Shale reservoirs typically exhibit the characteristics of large burial depth, low permeability, and well-developed bedding. Despite the implementation of water-based fracturing techniques with high displacement, attaining the optimal reservoir stimulation effect remains a formidable challenge. Consequently, in recent years, a novel method of hybrid fracturing method using pre-CO2 + sand-carrying slick water has been proposed. Within this framework, the meticulous design of fracturing operation parameters stands out as a pivotal factor influencing the efficacy of reservoir stimulation. This paper presents a coupled 3-D hydraulic fracture propagation model, investigating the influence of injection displacement, injection volume, and three-stage pre-injection fluid volume ratio on the fracture propagation. Two quantitative evaluation metrics, namely fracture surface area S and fracture transverse plane fractal dimension D, are precisely defined. Additionally, real-time analysis of bottom-hole pressure is conducted to further comprehend the dynamics of the process. Numerical investigations have revealed that both pre-CO2 injection displacement and volume exert a substantial influence on fracture half-length and complexity. Furthermore, the proportion of hybrid fracturing fluid demonstrates a notable impact on fracture height and complexity. The design of injection parameters does not exhibit a significant effect on the formation breakdown pressure. This study quantitatively evaluates fracture propagation under different working conditions from the perspective of fracture morphology, and serve as practical guidance for optimizing the process parameters design for hybrid fracturing in shale oil reservoirs, Ordos Basin.

Assessing input parameter hyperspace and parameter identifiability in a cardiovascular system model via sensitivity analysis

We aim to clarify our understanding of the process of state-space model input parameter identification, known, within the clinical context, as model personalisation. To do so, we apply reference sensitivity and identifiability techniques to a lumped parameter, single ventricle representation of the systemic circulation, chosen in view of its relative simplicity and prior art. We attempt to quantify the reliability of input parameter identifiability through the lens of 4 clinically relevant measurements and the attendant difficulty in personalising the model. In turn, this that we extend existing methods which combine both parameter influence and orthogonality, to global sensitivities. By examining different parameter sensitivity evaluation methodologies, we investigate the stability of optimal parameter subsets which are commonly used to aid clinical investigations. In order to perform the personalisation process, one must understand the complexity of the high dimensional input parameter hyperspace associated with this class of model. By utilising Sobol indices, we propose a domain-agnostic and intuitive approach. This involves varying the bounds of the input parameter space relative to the model’s base state. These investigations yield a pseudo-mapping of the input hyperspace, cementing our understanding of the role of identifiable input parameters in the state-space model. Our findings suggest a novel global methodology for input parameter identifiability and input hyperspace mapping, providing valuable insights into solving the personalisation process.

A Parameter Optimization Design Method for Single-Phase Dual Active Bridge AC-DC Converter

The single-stage dual active bridge (DAB) AC-DC converter has the advantages of high power density, low cost, and simple control; it has a broad potential for application in the field of onboard chargers (OBC). However, the lack of fast and accurate quantitative parameter optimization design methods in single-stage DAB AC-DC converters limits the overall efficiency of the converter. Based on the above problem, in order to improve the overall operating efficiency of the converter by optimizing the parameter transformer ratio and power inductance, this paper proposes a parameter design method considering a multi-timescale strategy by combining the steady-state analysis model of the converter in the line cycle and switching cycle and step-by-step reducing its design space through the constraints on the parameters. The first step is to obtain a safe design space for the parameters under the converter’s transmitted power and current stress constraints. The second step obtains the optimization design space of the parameters under the optimization of conduction loss and switching loss of the converter. Finally, the optimal parameters are determined by the loss analysis model. The proposed parameter optimization method entirely takes into account the steady-state characteristics of the DAB AC-DC converter during the line cycle, and the step-by-step constraints greatly accelerate the parameter design process. In addition, the proposed parameter optimization design method applies to all types of single-stage DAB AC-DC converters, which can be well applied to engineering practice.

Friction stir welding and processing of pipes: a status report on joining, materials, and industry applications

Purpose The purpose of this paper is to compile a comprehensive status report on pipes/piping networks across different industrial sectors, along with specifications of materials and sizes, and showcase welding avenues. It further extends to highlight the promising friction stir welding as a single solid-state pipe welding procedure. This paper will enable all piping, welding and friction stir welding stakeholders to identify scope for their engagement in a single window. Design/methodology/approach The paper is a review paper, and it is mainly structured around sections on materials, sizes and standards for pipes in different sectors and the current welding practice for joining pipe and pipe connections; on the process and principle of friction stir welding (FSW) for pipes; identification of main welding process parameters for the FSW of pipes; effects of process parameters; and a well-carved-out concluding summary. Findings A well-carved-out concluding summary of extracts from thoroughly studied research is presented in a structured way in which the avenues for the engagement of FSW are identified. Research limitations/implications The implications of the research are far-reaching. The FSW is currently expanding very fast in the welding of flat surfaces and has evolved into a vast number of variants because of its advantages and versatility. The application of FSW is coming up late but catching up fast, and as a late starter, the outcomes of such a review paper may support stake holders to expand the application of this process from pipe welding to pipe manufacturing, cladding and other high-end applications. Because the process is inherently inclined towards automation, its throughput rate is high and it does not need any consumables, the ultimate benefit can be passed on to the industry in terms of financial gains. Originality/value To the best of the authors’ knowledge, this is the only review exclusively for the friction stir welding of pipes with a well-organized piping specification detailed about industrial sectors. The current pipe welding practice in each sector has been presented, and the avenues for engaging FSW have been highlighted. The FSW pipe process parameters are characteristically distinguished from the conventional FSW, and the effects of the process parameters have been presented. The summary is concise yet comprehensive and organized in a structured manner.

Biogas Production Optimization in the Anaerobic Codigestion Process: A Critical Review on Process Parameters Modeling and Simulation Tools

Many operational parameters, either discretely or collectively, can influence the biodegradation performance towards enhancing biogas yield and quality. Among the operating parameters, organic loading rate (OLR), inoculum-substrate ratio, and carbon-nitrogen ratio (C/N) are the most critical parameters in the optimization and enhancement of biogas yield. Optimization of the biogas production processes depends on the ability of anaerobic microorganisms to respond to variations in operational parameters such as pH, redox potential, and intermediate products to enhance the biogas yield. This review article focuses on the role of process parameters, kinetic models, artificial intelligence, Aspen Plus (AP), and anaerobic digestion model no. 1 (ADM1) in optimizing biogas yield via an anaerobic codigestion (AcoD) process. The review showed that biomaterials codigestion upgraded biogas yield to the extent of 400%, and organic removal efficiency reached up to 90% compared to a single substrate. In addition, the current work has verified that the kinetic model is the most effective tool for signifying that the hydrolysis phase is the rate-limiting step, whereas AP is the most effective tool in the design and optimization of the AcoD process parameters. The reviewed kinetic and AI models show strong correlation values ranging from 0.931 to 0.9991 and 0.8700 to 0.9998, respectively. The AcoD system involves complex chemical reactions, but AP might have limitations in representing such complex chemical processes with nonideal behavior and complicated reaction mechanisms. The design and optimization of AcoD with reliable input parameters are highly limited or nonexistent. The AcoD process design with AP opens fresh research opportunities, including improved efficiency, finding appropriate retention time, and saving time, as well as finding the optimum biogas yield. This review article gives an insightful understanding of AcoD process parameter optimization and valuable strategies for policy development enhancing sustainability in the biogas sector.

Simulation method for filling stage in liquid composite molding based on a two-phase flow volume of fluid model

Liquid composite molding (LCM) is a process for manufacturing fiber-reinforced resin matrix composites. The chemical rheological properties of resin in the filling stage of LCM will produce a non-isothermal flow process that includes multi-field coupling of thermal field, flow field, and chemical field. During the filling stage, the resin is injected into a closed mold pre-laid with a fiber at a certain temperature and pressure to flow continuously and undergo a curing chemical reaction. The viscosity of resin changes constantly under the influence of temperature and the curing reaction, which affects the flow of resin. The simulation of this multi-field coupling filling stage in LCM can provide an effective reference for the design of process parameters. The simulation method of the filling stage in LCM is studied in this paper. The mathematical model of the filling stage is derived based on the air–resin two-phase flow volume of the fluid model. Based on the secondary development of the ANSYS FLUENT software, the mathematical model is solved to realize the simulation. The parameters of an alkali-free glass fiber plain weave fabric are measured and the calculation model of thermosetting resin viscosity is established by designed experiments, which are applied in simulation. The filling stage experiment of LCM is designed to verify the accuracy of simulation results. Meanwhile, by comparing with the traditional simulation method, it is found that the data error obtained by the simulation method in this paper is significantly reduced.

Human behaviour simulation for promoting usefulness and user-centric values in parametric design

Although contemporary architectural design education adopts the parametric design approach, which involves complex iterations between the arrangement of form components and building performance, this method limits students' exploration of the optimal matches between generated form and the behaviours of prospective users. Therefore, as a solution, this study hypothesises the effects of human behaviour simulation on students' exploration of user-centric values in the parametric design process. This paper compares the self-evaluation scores of 90 undergraduate students before and after simulation. Data were collected during an authentic digital design course that examined shopfront façade prototypes. Statistical analysis and interpretation indicate that simulation better facilitates students' search for matches between initially value-isolated forms, created using a parametric design tool, and useful user-centric values. However, simulation is not relevant for discovering unexpected forms. This study addresses the unknown affordances in simulation when assigning user-related value to form generation that lacks value.

Intelligent optimization design of squeeze casting process parameters based on neural network and improved sparrow search algorithm

Squeeze casting process parameters are the key to squeeze casting production and to obtain excellent performance casts. To realize intelligent optimization design of process parameters under various requirements, this work presents a new intelligent optimization design framework for squeeze casting process parameters based on process data and integrating a two-stage intelligent integrated optimization. To adapt to diverse optimization applications of different process and target parameters, the backpropagation (BP) neural network and existing process data are utilized to intelligently establish the incompletely determined correlations between process parameters and squeeze cast quality or properties. An improved sparrow search algorithm (LCSSA) is developed and integrated to optimize both the aforementioned model structure and intelligently obtain the optimal solution, that is, the two stages of optimization. For the purpose of assessing the effect of each performance on the combination of process parameters, the information entropy weight approach is used. Various application cases and experiments have been conducted to evaluate the effectiveness of the suggested intelligent optimization design framework. It is indicated that the proposed method is practicable and can intelligently achieve ideal process parameters with high accuracy even based on small-scale data samples. The solving efficiency and optimization accuracy of LCSSA are superior than those of other intelligent optimization algorithms like the genetic algorithm (GA). The proposed framework outperforms the mixture of the BP neural network and other traditional optimization algorithms.

Optimizing refuse-derived fuel production from scheduled wastes through Aspen plus simulation

This study aims to improve the quality of fuel with high calorific value namely Sfuel – a commercial high-quality refuse-derived fuel (RDF) from hazardous waste via modifying the process design and operating parameters of thermal conversion process. The study analyses key parameters of RDF quality, such as calorific value and heavy metal content, before and after process modifications based on the combination of experimental and simulation using Aspen Plus. In this study, the temperature and pressure of the simulation system are varied from 100 to 700 °C and from 1 to 5 bar, respectively. Findings indicate that there are a total of eleven heavy metals and 179 volatile compounds in the “Sfuels”. The quality of the targeted product is greatly improved by the metal evaporation at high temperatures and pressures. However, the calorific value of RDF significantly decreases at 700 °C due to a large amount of the carbon content being evaporated. Although the carbon content at high temperatures is significantly lost, the heat from the vapour stream reactor outlet, which is reused to preheat the nitrogen gas stream supplied to the system, reduces energy consumption while improving the thermal conversion efficiency of the system. Besides, low pressure along with high temperature are not the optimal conditions for quality Sfuels improvement by thermal conversion. Results also indicate that electric heating is more economically efficient than natural gas heating.

Dynamic Industrial Optimization: A Framework Integrates Online Machine Learning for Processing Parameters Design

We develop the online process parameter design (OPPD) framework for efficiently handling streaming data collected from industrial automation equipment. This framework integrates online machine learning, concept drift detection and Bayesian optimization techniques. Initially, concept drift detection mitigates the impact of anomalous data on model updates. Data without concept drift are used for online model training and updating, enabling accurate predictions for the next processing cycle. Bayesian optimization is then employed for inverse optimization and process parameter design. Within OPPD, we introduce the online accelerated support vector regression (OASVR) algorithm for enhanced computational efficiency and model accuracy. OASVR simplifies support vector regression, boosting both speed and durability. Furthermore, we incorporate a dynamic window mechanism to regulate the training data volume for adapting to real-time demands posed by diverse online scenarios. Concept drift detection uses the EI-kMeans algorithm, and the Bayesian inverse design employs an upper confidence bound approach with an adaptive learning rate. Applied to single-crystal fabrication, the OPPD framework outperforms other models, with an RMSE of 0.12, meeting precision demands in production.

Adaptive Anti-Surge Control Strategy for PEM Fuel Cell Vehicle With Online Surge Detection

In this study, an online adaptive anti-surge control (OAASC) strategy is proposed for proton exchange membrane (PEM) fuel cell system. Insufficient attention has been given to the issue of the surge in the air supply system, which can damage the compressor and stack, reducing the fuel cell vehicle’s efficiency and lifespan. The OAASC approach employs a multiple time-domain sliding window technique and characteristic energy-based correction to detect and adaptively contain surges before they become serious. A dynamic model of the fuel cell air supply system with surge prediction is established using a two-stage parameter identification process and is verified through 60 kW fuel cell engine experiments. Simulation and experimental comparisons between OAASC and standard industrial methods are conducted to evaluate the proposed method’s performance. The experimental results demonstrate that the proposed strategy improves control performance, with surges being detected within 0.2 s and recovering within 0.5 s.

Effect of high-frequency dynamic characteristics in the ultrasonic surface rolling process on the surface properties

Ultrasonic surface rolling process (USRP) is a combination of ultrasonic impact and deep rolling techniques. However, the addition of ultrasonic vibrations complicates the working mechanism of USRP tools. Existing studies rarely consider the evolution of dynamic characteristics in USRP tools resulting from changes in ultrasonic amplitudes, which undoubtedly exert a significant influence on surface properties. Moreover, they typically limit the ultrasonic amplitude to a small range. This study aims to identify the boundary process parameters to differentiate between stable and unstable dynamic characteristics during USRP and to compare their effects on the surface integrity and the plastic deformation. Therefore, an equivalent USRP tool model was proposed to analyse the working mechanism at a large-scale ultrasonic amplitude. The boundary USRP parameters responsible for different dynamic characteristics were experimentally explored. Finally, comparative USRP experiments on the Ti-6Al-4 V alloy were conducted, and it was observed that USRP parameters under stable dynamic characteristics were beneficial to the excellent surface roughness. Conversely, altering the ultrasonic amplitude to attain unstable dynamic characteristics leads to a notable degradation in the surface quality; however, the extent of plastic deformation is significantly increased. This study provides insights into the effects of dynamic characteristics in USRP, thereby contributing to a deeper understanding of the process and facilitating effective design of process parameters.

Adaptive unscented Kalman filter methods for identifying time‐variant parameters via state covariance re‐updating

AbstractThe conventional parameter identification process generally assumes that parameters remain constant. However, under extreme loading conditions, structures may exhibit nonlinear behavior, and parameters could demonstrate time‐variant characteristics. The unscented Kalman filter (UKF), as an efficient online recursive estimator, is widely used for identifying parameters of nonlinear systems. Nevertheless, it exhibits limitations when attempting to identify time‐variant parameters. To address this issue, this paper proposes a covariance matching technique that produces an array of adaptive UKF algorithms. Firstly, the sensitivity parameter η is defined to identify the instant when the parameter change occurs, and its threshold is calculated based on the sensitivity parameter time history curve. Secondly, an adaptive forgetting factor is introduced to simultaneously update the innovation, cross, and state covariance matrices when the kth‐step sensitive parameter surpasses the threshold. Finally, a secondary correction forgetting factor (SCFF) is employed to further re‐update the state covariance values at the identified damage locations. This creative step enhances the adaptive capability and optimizes the identification accuracy of the proposed algorithms. Both the numerical simulations and shaking table test demonstrate that the proposed adaptive algorithms can efficiently identify the time‐variant stiffness‐type parameters, and accurately capture their time‐variant characteristics.

Research on the Temperature Distribution in Electrically Heated Offshore Heavy Oil Wellbores

The electric heating process for lifting heavy oil has been widely applied. However, research on its temperature field laws mostly focuses on onshore heavy oil wells, while research offshore is limited. Therefore, based on the energy conservation equation and heat transfer theory, a transient one-dimensional wellbore temperature model coupled with the temperature and viscosity of heavy oil and considering the effect of time was developed. In order to verify the accuracy of the model, the results of the previous model were used for comparison with the present model, and the results showed that the model has good accuracy. The results show that a reasonable selection of the process parameters of electric heating can increase the production of heavy oil while saving development costs and improving the economic benefits of the oilfield. The conclusions and recommendations of this paper can provide a theoretical basis and guiding suggestions for the optimal design of process parameters for lifting heavy oil using an offshore electric heating process.

Design and analysis of a wall-climbing robot with passive compliant mechanisms to adapt variable curvatures walls

AbstractMotivated by practical applications of inspection and maintenance, we have developed a wall-climbing robot with passive compliant mechanisms that can autonomously adapt to curved surfaces. At first, this paper presents two failure modes of the traditional wall-climbing robot on the variable curvature wall surface and further introduces the designed passive compliant wall-climbing robot in detail. Then, the motion mechanism of the passive compliant wall-climbing robot on the curved surface is analyzed from stable adsorption conditions, parameter design process, and force analysis. At last, a series of experiments have been carried out on load capability and curved surface adaptability based on a developed principle prototype. The experimental results indicated that the wall-climbing robot with passive compliant mechanisms can effectively promote both adsorption stability and adaptability to variable curvatures.