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In practical applications, an engineer is sometimes expected to execute the step test for tuning the controller without waiting much for the steady-state or a low level of disturbances. Hence, knowing that the initial settings may not be quite reliable, he/she detunes the controller by reducing its gain as a precaution against possible poor behaviour of the closed-loop system. It is up to their experience to choose by how much to detune. Therefore, the development of a practically oriented approach that would assist the engineer to choose the degree of gain reduction is the goal of this paper. The approach assumes that process parameters are determined by the least-squares approximation of the step response. Accuracy of the approximation is evaluated by a relative approximation error involving integrals of the error and the process response itself. The SIMC tuning rules are applied to choose the initial controller settings. The approach relies on detecting by simulation the worst case that may happen when the step response is triggered at any time. Detuning nomograms specify by how much to reduce the initial gain for PI-FOPTD and PID-SOPTD designs, given the relative approximation error. Two long-lasting lab experiments involving temperature control identify a plant, verify the load disturbance model through multiple step tests and demonstrate usage of the approach in the closed-loop system. [ABSTRACT FROM AUTHOR]

With increasing globalisation, organisations are confronted with intensifying market competition. Consequently, organisations continually implement novel technologies to address the challenges posed by market competition. In business process management (BPM), robotic process automation (RPA) is regarded as a tool or methodology that can automate specific tasks or activities within a business process. RPA is widely employed as a standard solution in various industries to assist enterprises in automating their processes, enhancing their operational efficiency, and reducing operational costs. Nevertheless, it is prudent to consider which processes can be automated by applying RPA and the most appropriate deployment methods for RPA. A literature review reveals that process identification is the optimal method for identifying processes suitable for automation, and three approaches - process mining, process quality-based and interview-based can be considered. There are three principal models for deploying RPA: local, cloud and hybrid. The RPA managers in an organisation are thus able to select the most appropriate deployment option based on the organisation's policies, requirements, and cost considerations. Furthermore, this study identifies the critical success factors (CSFs) and analyses their relationships using a 'why-why' diagram. This study could assist organisations in a more comprehensive understanding of RPA technology, which enables more effective utilisation of RPA for enhanced process management. [ABSTRACT FROM AUTHOR]

This study introduces a novel process identification method aimed at overcoming the challenge of accurately estimating process models when faced with deterministic disturbances, a common limitation in conventional identification methods. The proposed method tackles the difficult modeling problems due to deterministic disturbances by representing the disturbances as a linear combination of Laguerre polynomials and applies an integral transform with frequency weighting to estimate the process model in a numerically robust and stable manner. By utilizing a least squares approach for parameter estimation, it sidesteps the complexities inherent in iterative optimization processes, thereby ensuring heightened accuracy and robustness from a numerical analysis perspective. Comprehensive simulation results across various process types demonstrate the superior capability of the proposed method in accurately estimating the model parameters, even in the presence of significant deterministic disturbances. Moreover, it shows promising results in providing a reasonably accurate disturbance model despite structural disparities between the actual disturbance and the model. By improving the precision of process models under deterministic disturbances, the proposed method paves the way for developing refined and reliable control strategies, aligning with the evolving demands of modern industries and laying solid groundwork for future research aimed at broadening application across diverse industrial practices. [ABSTRACT FROM AUTHOR]

Business Process mapping (BP mapping) is important for a company to identify their activities. Previous research suggests several approaches for process identification and BP mapping, which would be easier if the company had already implemented a computer-based information system. The research presented in this paper has the purpose of providing an alternative method for BP mapping especially for the company that does not implement the computer-based information system. A proposed method is using job description documents that the company had to identify elements needed to perform BP mapping which are actor, process, document, and flow of documents. A Natural Language Process (NLP) which is text mining method is used for mining job documents to identify those elements that exist in each job position. To illustrate the applicability of the proposed method, samples of job descriptions of 15 companies are taken. It shows that the proposed method can be applied. [ABSTRACT FROM AUTHOR]

Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal Research Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study introduces a novel method for identifying dynamic systems aimed at deriving reduced-fractional-order models. Applicable to processes exhibiting an S-shaped step response, the method effectively characterizes fractional behavior within the range of fractional orders (α∈[0.5,1.0]). The uniqueness of this approach lies in its hybrid nature, combining one-variable optimization techniques for estimating the model fractional order α with analytical expressions to estimate parameters T and L. This hybrid approach leverages information from the reaction curve obtained through an open-loop step-test experiment. The proposed method demonstrates its efficacy and simplicity through several illustrative examples, showcasing its advantages over established analytical and optimization-based techniques. Notably, the hybrid approach proves particularly advantageous compared to methods relying on the process reaction curve. To highlight its practical applicability, the identification algorithm based on this hybrid approach is implemented on hardware using a microprocessor. The experimental prototype successfully identifies the First-Order Plus Dead Time (FFOPDT) model of a thermal-based process, validating the proposed method's real-world utility.

Purpose: Carry out a detailed analysis of existing processes classifications and propose a new one, the main focus of which will be on the unique requirements of the functioning of the penitentiary system, which is the totality of the institutions of penitentiary law and the applicable legislation. In addition, the article also aims to point out the differences between the classical view of identifying and defining processes carried out in typical organisations and those carried out in prisons. Design/methodology/approach: The objectives of the article were achieved through an in-depth analysis of the literature on the identification and classification of processes carried out in typical organisations and those carried out in prisons and a modification of Porter's classic value chain model. Furthermore, throughout his career, the author has been investigating the functioning of the penitentiary system in terms of executing logistical processes over the past few years. Findings: The author of the article compared the existing classifications of processes and suggested a new one more fitting to the functioning of the penitentiary system that defines the rules of the prisons that carry out the execution of imprisonment. Originality/value: The originality of the article lies in the modification of the classic Porter's value chain model, illustrating the organization's activities as a systematic sequence of actions aimed at delivering the final product to the end user in the form of the proper implementation of imprisonment in prisons. In the face of dynamically changing challenges posed by the contemporary penitentiary system, understanding and properly classifying the implemented processes become crucial for effective management and improvement of the quality of the implemented actions [ABSTRACT FROM AUTHOR]

Water-soluble polymers are widely employed as additives in many different industries. The need to treat sewage contaminated with water-soluble polymers is essential to prevent persistent pollutants from entering our environment. The advanced oxidation process (AOP), UV/H 2 O 2 process, is used in this study to degrade polyvinyl alcohol (PVA) in aqueous solutions. However, a suitable modeling approach and a control scheme are required to remove the PVA polymer and reduce hydrogen peroxide (H 2 O 2) residual in the treated effluent within a safe level to prevent adverse effects on the aquatic system as well as subsequent biological processes. This study presents black-box modeling for identifying the dynamics of polyvinyl alcohol (PVA) degradation in a series of UV/H 2 O 2 photo-reactors, where the process input and response variables are inlet hydrogen peroxide concentrations and effluent pH, respectively. Data processing, model development, and process simulation are performed using MATLAB R2019b software. In this study, the linear AutoRegressive with eXogenous input (ARX), non-linear ARX (NARX), and Hammerstein-Wiener models are considered as base system models, where the sigmoid-network-based NARX produced the best representation with 82.24% of the trainset and 76.28% of the validation-set of the process dynamics. The design of PID controllers tuned using ARX and sigmoid-network-based NARX models are discussed, and the controller performance is analyzed for set-point tracking and disturbance rejection through simulation studies. The closed-loop response of ARX-PID and NARX-PID are deemed adequate. In fact, the NARX-PID performs much better for the studied process achieving an integrated absolute error (IAE) of 0.6211 with a settling time of 4.63 h, whereas the ARX-PID has lower IAE (0.4238) and produces a more aggressive controlled output response robust in disturbance rejection. Thus, ARX-PID is adequate for frequent process disturbances but less suitable for process set-point tracking. • Development of black-box modeling for identifying dynamics of PVA degradation in a UV/H 2 O 2. • Design of a conventional controller and tuning to minimize H 2 O 2 residual in UV/H 2 O 2 process. • Data-driven linear and nonlinear system identification and controller design applied to UV/H 2 O 2. • Controller parameters were optimized for set-point tracking for the H 2 O 2 residual. • Controller parameter optimization for input, H 2 O 2 dosage, disturbance rejection was studied. [ABSTRACT FROM AUTHOR]

The aim of this work was to determine the effectiveness of using the FMEA method in the traditional and modern versions for a small company producing plastic toys. It also identified the production process of a plastic instrument toy selected for analysis by dividing operations into adding and not adding value, in accordance with the Lean concept. Through the FMEA sheets constructed and presented in this work in both versions of this method, the causes and effects of defects in the technological operations of the production process of the test subject were presented. A risk analysis was carried out using assessments available for the traditional and new versions, and preventive and detective actions were proposed. The result of the work was a comparison of risk indicators characteristic for both versions of the FMEA method and an assessment of the effectiveness of this method in the analysed enterprise. Emphasis was placed on the human factor in the research. Both the division of operations into adding and not adding value, as well as the FMEA method, were useful for further optimization and process improvement activities. [ABSTRACT FROM AUTHOR]

This paper presents a new method for identifying dynamical systems to get fractional-reduced-order models based on the process reaction curve. This proposal uses information collected from the process. It can be applied to processes with an S-shaped step response that can be considered with fractional behavior and a fractional order range of $\alpha \in [{0.5, 1.0}]$ . The proposed approach combines obtaining the fractional order of the model using asymptotic properties of the Mittag-Leffler function with time-based parameter estimation by considering two arbitrary points on the process reaction curve. The improvement in terms of accuracy of the identified FFOPDT model is obtained due to a more accurate estimation of $\alpha $ parameter. This method is characterized by its effectiveness and simplicity of implementation, which are key aspects when applying at an industrial level. Several examples are used to illustrate the effectiveness and simplicity of the proposed method compared to other well-established methods and other approaches based on the process reaction curve. Finally, it is also implemented on microprocessor-based hardware to demonstrate the applicability of the proposed method to identify the fractional model of a thermal process.

System identification is the field of systems mathematical modeling from experimental data. In the modeling chain, experiments realization, model structure selection and parameter estimation are essential steps. However, performing tests for system identification is time-consuming and not always possible. Also, while in most modeling workflows model parameters are optimized, model structure is generally selected manually, and if optimized, data informativity is not considered. For these reasons, an approach using historical data with optimized selection of model's structure and parameters, considering data informativity, is proposed. Therefore, historical data is collected and data around the process operating point is selected. Then, data is prefiltered, resampled, cleaned, and normalized. Then, for modeling, a vector autoregressive with exogenous variables model structure is considered. To estimate the model's structural parameters and coefficients, a bi-level optimization algorithm is proposed. For the outer optimization, genetic algorithm, fireworks algorithm, particle swarm optimization, and simulated annealing algorithm are simulated and compared to conventional methods. Also, as normal operation data is used, a data informativity step is integrated to discard non-informative model structures. To estimate the model coefficients, ridge regression is used. In a case study, the approach is applied to model an industrial thickener. The obtained results show that the proposed approach outperforms conventional ones. [ABSTRACT FROM AUTHOR]

For the identification of processes in lithium-ion batteries (LIB) by electrochemical impedance spectroscopy, frequency data is often transferred into the time domain using the method of distribution of relaxation times (DRT). As this requires regularization due to the ill-conditioned optimization problem, the investigation of data-driven methods becomes of interest. One promising approach is the Loewner method (LM), which has already had a number of applications in different fields of science but has not been applied to batteries yet. In this work, it is first deployed on synthetic data with predefined time constants and gains. The results are analyzed concerning the choice of model order, the type of processes , i.e., distributed and discrete, and the signal-to-noise ratio. Afterwards, the LM is used to identify and analyze the processes of a cylindrical LIB. To verify the results of this assessment a comparison is made with the generalized DRT at two different states of health of the LIB. It is shown that both methods lead to the same qualitative results. For the assignment of processes as well as for the interpretation of minor gains, the LM shows advantageous behavior, whereas the generalized DRT shows better results for the determination of lumped elements and resistive–inductive processes. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for modelling nonlinear dynamic processes (NDP). It is based on a nonlinear autoregressive with exogenous (NARX) inputs model structure and a deep convolutional fuzzy system (DCFS). The DCFS is a hierarchical fuzzy structure, which can overcome the deficiency of general fuzzy systems when facing high dimensional data. For relieving the curse of dimensionality, as well as improving approximation performance of fuzzy models, we propose combining the NARX with the DCFS to provide a good approximation of the complex nonlinear dynamic behavior and a fast-training algorithm with ensured convergence. There are three NARX DCFS structures proposed, and the appropriate training algorithm is adapted. Evaluations were performed on a popular benchmark—Box and Jenkin's gas furnace data set and the four nonlinear dynamic test systems. The experiments show that the proposed NARX DCFS method can be successfully used to identify nonlinear dynamic systems based on external dynamics structures and nonlinear static approximators. [ABSTRACT FROM AUTHOR]

This study aims to develop a pipeline for identifying and prioritising the processes to include in business process management (BPM) initiatives by considering the obstacles for giving equal importance to the processes performed in businesses. The paper proposes the use of a process classification framework as a reference for process identification and fuzzy multi‐criteria decision‐making methods for prioritising the processes. A real‐life application is presented, which adopts APQC Process Identification Framework, Fuzzy‐SWARA for weighting the criteria, and six additional methods for prioritising the processes. The approach would assist the executives in maintaining the BPM lifecycle by presenting a roadmap, especially for organisations that do not have much experience in this field. Methodological depth, richness and recency would provide guidelines for investigating the best set of processes to include in the architecture initially, not in the analysis or redesign phases. [ABSTRACT FROM AUTHOR]

This paper aims to present a general identification procedure for fractional first-order plus dead-time (FFOPDT) models. This identification method is general for processes having S-shaped step responses, where process information is collected from an open-loop step-test experiment, and has been conducted by fitting three arbitrary points on the process reaction curve. In order to validate this procedure and check its effectiveness for the identification of fractional-order models from the process reaction curve, analytical expressions of the FFOPDT model parameters have been obtained for both situations: as a function of any three points and three points symmetrically located on the reaction curve, respectively. Some numerical examples are provided to show the simplicity and effectiveness of the proposed procedure. Good results have been obtained in comparison with other well-recognized identification methods, especially when simplicity is emphasized. This identification procedure has also been applied to a thermal-based experimental setup in order to test its applicability and to obtain insight into the practical issues related to its implementation in a microprocessor-based control hardware. Finally, some comments and reflections about practical issues relating to industrial practice are offered in this context. [ABSTRACT FROM AUTHOR]

The dynamic model of the milk cooling process from 36°C to 4°C using chilled water available at 2°C has been carried out. The cooling water temperature is kept constant by using a refrigeration unit. The process being studied was a Packo brand milk cooling tank belonging to KUD SAE Pujon (Malang - Indonesia). A fundamental heat balance method was used to derive the model, leading to a first-order transfer function process. For a 2 hours cooling process then, the gain and time constant values are 1.00 and 42.3548 mins respectively, or G(S) = ... (first order process). Deriving system transfer function through a mechanistic model is considered difficult; therefore, in this paper, we explored process identification via Auto Regressive eXogenous (ARX). Transient simulations could then be performed to identify the dynamic behavior of the cooling process. The system was then identified using several orders of the Auto Regressive eXogenous (ARX) model, and then the results were re-tested on different forms of perturbations and obtained quite accurate results. The transfer function identified through the ARX111 is G(S) = ... (first order process), while via ARX441, the 5th order process was obtained: G(S) = ... These models particularly useful for process control design and analysis. [ABSTRACT FROM AUTHOR]

In view of the problems of high cost and low reliability in obtaining operation information such as flow rate and pressure of offshore natural gas production system, research on soft sensing is carried out, and a dynamic data‐driven model bank is established, in purpose of estimating single‐well flow rate and wellhead pressure, providing convenience tool for online monitoring and system safety analysis. Combining dynamic and steady‐state samples, introducing black‐box identification techniques including orthogonal least square regression and deep learning along with parameter correction techniques such as bi‐objective least square algorithm and transfer learning, a series of nonlinear auto‐regressive models with exogenous inputs (NARX) are built, consisting of black‐box and gray‐box polynomial NARX (Poly‐NARX) models as well as deep neural network NARX (DNN‐NARX) models, approximately describing the dynamic performance of gas production well. Through realistic operation data, the simulation results of Poly‐NARX, DNN‐NARX, and multiple‐layer‐perception‐NARX models are compared. It is observed that gray‐box DNN‐NARX model shows the best performance with advantages of higher global applicability, better approximation ability, and stronger generalization ability. Proposed model bank is of high expansibility and engineering applicability for soft sensing problems in the petroleum industry, laying the ground work for building smart oil and gas field. [ABSTRACT FROM AUTHOR]

In this paper, a data mining based methodology for process identification from historical data was proposed. Thereon, it considers the phases of process understanding, data collection, data preparation, data modeling, and model evaluation. As some parts of historical data are irrelevant, a data selection step, based on the Gaussian Mixture Model (GMM) clustering algorithm, was considered. Additionally, the methodology includes a data informativity step to study the richness of data. In this regard, the condition number (CN) and the extended CN for ridge regression (RR CN) were used. To evaluate the approach, 2 years of industrial thickener historical data were used. Thereafter, data were prepared and an ARX (Auto-Regressive with eXogenous inputs) model structure was adopted to identify the model. To estimate input delays, Granger causality was used. As for fit criteria, least square regression was tested and compared to ridge regression as a less sensitive method to multicollinearity. The results were then evaluated based on the 20-step ahead prediction and compared to existing methods. In this context, the proposed approach gave the best results with an R2 of 98.11% and 62.70% for 1 and 20-step ahead predictions, respectively. [ABSTRACT FROM AUTHOR]

Abstract In view of the problems of high cost and low reliability in obtaining operation information such as flow rate and pressure of offshore natural gas production system, research on soft sensing is carried out, and a dynamic data‐driven model bank is established, in purpose of estimating single‐well flow rate and wellhead pressure, providing convenience tool for online monitoring and system safety analysis. Combining dynamic and steady‐state samples, introducing black‐box identification techniques including orthogonal least square regression and deep learning along with parameter correction techniques such as bi‐objective least square algorithm and transfer learning, a series of nonlinear auto‐regressive models with exogenous inputs (NARX) are built, consisting of black‐box and gray‐box polynomial NARX (Poly‐NARX) models as well as deep neural network NARX (DNN‐NARX) models, approximately describing the dynamic performance of gas production well. Through realistic operation data, the simulation results of Poly‐NARX, DNN‐NARX, and multiple‐layer‐perception‐NARX models are compared. It is observed that gray‐box DNN‐NARX model shows the best performance with advantages of higher global applicability, better approximation ability, and stronger generalization ability. Proposed model bank is of high expansibility and engineering applicability for soft sensing problems in the petroleum industry, laying the ground work for building smart oil and gas field.

The significance of insurance activity is constantly growing, generating new problems and posing new challenges. One of these challenges is meeting the growing requirements and expectations of customers. This requires the efficient management of insurance companies, which means the necessity to resort to modern management concepts, particularly the concept of process management and its related instruments. The article presents the results of research carried out at the Faculty of Management of the Warsaw University of Technology regarding process management in insurance companies. The distinctness of insurance activity and its financial management is discussed and its following aspects presented: the identification of insurance activity processes encompassing the management of basic and auxiliary processes; the model of the financial management process of insurance companies; the relationship between the financial management process and other processes implemented in insurance companies; financial situation assessment measures for insurance companies, and the financial management process.

For the identification of processes in lithium-ion batteries (LIB) by electrochemical impedance spectroscopy, frequency data is often transferred into the time domain using the method of distribution of relaxation times (DRT). As this requires regularization due to the ill-conditioned optimization problem, the investigation of data-driven methods becomes of interest. One promising approach is the Loewner method (LM), which has already had a number of applications in different fields of science but has not been applied to batteries yet. In this work, it is first deployed on synthetic data with predefined time constants and gains. The results are analyzed concerning the choice of model order, the type of processes , i.e., distributed and discrete, and the signal-to-noise ratio. Afterwards, the LM is used to identify and analyze the processes of a cylindrical LIB. To verify the results of this assessment a comparison is made with the generalized DRT at two different states of health of the LIB. It is shown that both methods lead to the same qualitative results. For the assignment of processes as well as for the interpretation of minor gains, the LM shows advantageous behavior, whereas the generalized DRT shows better results for the determination of lumped elements and resistive–inductive processes.

This paper presents a new approach for modelling nonlinear dynamic processes (NDP). It is based on a nonlinear autoregressive with exogenous (NARX) inputs model structure and a deep convolutional fuzzy system (DCFS). The DCFS is a hierarchical fuzzy structure, which can overcome the deficiency of general fuzzy systems when facing high dimensional data. For relieving the curse of dimensionality, as well as improving approximation performance of fuzzy models, we propose combining the NARX with the DCFS to provide a good approximation of the complex nonlinear dynamic behavior and a fast-training algorithm with ensured convergence. There are three NARX DCFS structures proposed, and the appropriate training algorithm is adapted. Evaluations were performed on a popular benchmark—Box and Jenkin’s gas furnace data set and the four nonlinear dynamic test systems. The experiments show that the proposed NARX DCFS method can be successfully used to identify nonlinear dynamic systems based on external dynamics structures and nonlinear static approximators.

Polyvinyl alcohol (PVA) is an emerging pollutant commonly found in industrial wastewater, owing to its extensive usage as an additive in the manufacturing industry. PVA's popularity has made wastewater treatment technologies for PVA degradation a popular research topic in industrial wastewater treatment. Although many PVA degradation technologies are studied in bench-scale processes, recent advancements in process optimization and control of wastewater treatment technologies such as advanced oxidation processes (AOPs) show the feasibility of these processes by monitoring and controlling processes to meet desired regulatory standards. These wastewater treatment technologies exhibit complex reaction mechanisms leading to nonlinear and nonstationary behavior related to variability in operational conditions. Thus, black-box dynamic modeling is a promising tool for designing control schemes since dynamic modeling is more complicated in terms of first principles and reaction mechanisms. This study seeks to provide a survey of process control methods via a comprehensive review focusing on PVA degradation methods, including biological and advanced oxidation processes, along with their reaction mechanisms, control-oriented dynamic modeling (i.e., state-space, transfer function, and artificial neural network modeling), and control strategies (i.e., proportional-integral-derivative control and predictive control) associated with wastewater treatment technologies utilized for PVA degradation. [ABSTRACT FROM AUTHOR]

The performance of two continuous UV/H2O2 photoreactors in series under unsteady-state condition is studied. An input–output dynamic model is constructed to describe the degradation of water-soluble polyvinyl alcohol (PVA) in photoreactors. Identification techniques, AutoRegressive with eXogenous input (ARX) and AutoRegressive Moving Average with eXogenous input (ARMAX), are employed to construct transfer functions. The simulation is carried out for an open-loop operation by applying step changes to the input variables (PVA and H2O2 inlet concentrations and PVA feed flow rate) using the developed model. For effluent total organic carbon (TOC) and H2O2 concentrations as responses, a model predictive control (MPC) scheme is developed to control the photoreactors to maintain desired values of process variables for setpoint and load changes. The closed-loop simulation results show that the multi-input/multi-output (MIMO) MPC produce a good performance for tracking setpoint changes in the TOC and H2O2 concentrates at the photoreactor effluent and it is also able to handle process interactions and constraints. The MPC controller successfully suppresses stochastic disturbances of the inlet PVA concentration. [ABSTRACT FROM AUTHOR]

This paper aims to present a general identification procedure for fractional first-order plus dead-time (FFOPDT) models. This identification method is general for processes having S-shaped step responses, where process information is collected from an open-loop step-test experiment, and has been conducted by fitting three arbitrary points on the process reaction curve. In order to validate this procedure and check its effectiveness for the identification of fractional-order models from the process reaction curve, analytical expressions of the FFOPDT model parameters have been obtained for both situations: as a function of any three points and three points symmetrically located on the reaction curve, respectively. Some numerical examples are provided to show the simplicity and effectiveness of the proposed procedure. Good results have been obtained in comparison with other well-recognized identification methods, especially when simplicity is emphasized. This identification procedure has also been applied to a thermal-based experimental setup in order to test its applicability and to obtain insight into the practical issues related to its implementation in a microprocessor-based control hardware. Finally, some comments and reflections about practical issues relating to industrial practice are offered in this context.

Additive Manufacturing (AM) is a novel manufacturing method where a component is fabricated by accumulating material layer-by-layer therefore facilitating customized near-net-shape components while maximizing design freedom. A metal AM technology suited for large-scale component fabrication that has been emerging in recent years and is commonly referred to as Wire and Arc AM (WAAM) uses Gas Metal Arc Welding (GMAW) technology to deposit material and large-scale robotic serial manipulator systems (reach >1 m cubed). One of the requirements for applying GMAW welding technology to AM is to identify and optimize deposition parameters in order to achieve a desired deposition quality measured in terms of geometrical, mechanical and metallurgical consistency. In this work, deposition process parameters qualitatively and quantitatively influencing the geometrical, mechanical and metallurgical consistency are identified and statistically validated. A deposition parameter combination is found that optimizes the quality of single-track wall benchmark components made from low-carbon steel. Moreover, correlations are found between accumulated heat during fabrication and the geometrical variations of the benchmark components due to bead slumping. Additionally, correlations between microstructure variations and geometrical variations are found. Finally, based on the presented analyses, in-situ temperature monitoring methods are proposed in order to achieve optimal component quality.

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Distribution of Relaxation Times (DRT) analysis gained considerable attention for its ability to reveal detailed information about complex electrochemical processes without requiring a priori knowledge. This review provides a comprehensive insight into different methods of the DRT analysis, their mathematical bases, and the latest approaches to acquiring and analyzing frequency and time domain data. The analysis is based on the deconvolution of frequency domain data into a distribution function of gains at (pre-specified) relaxation times in the time domain, which improves the spectral resolution and separability of electrochemical processes. It provides valuable information about different electrochemical processes on different time scales, making it particularly useful for the characterization of both materials and electrochemical systems. The DRT analysis can be applied to arbitrary spectra containing electromagnetic effects, resistive–capacitive processes, and solid-state diffusion. To enhance process identification, a post-processing step involving peak analysis with Gaussian or RQ-distribution peaks is presented and the assignment of peak patterns induced by distributed processes like solid-state diffusion is discussed. In addition, a step-by-step workflow for the DRT analysis is provided to guide researchers from data acquisition and validation techniques to calculation and interpretation of the distribution function. [Display omitted] • State-of-the-art methods for the Distribution of Relaxation Times analysis. • Discrete and regularization-based solution algorithms. • Probabilistic deconvolution method based on Gaussian Process optimization. • Data-driven approach based on the interpolatory Loewner Method. • Overview on measurement and data validation techniques. [ABSTRACT FROM AUTHOR]

Process models play an important role in the process industry. They are used for simulation purposes, quality control, fault detection, and control design. Many researchers have been engaged in model identification. However, it is difficult to find an analytical identification method that provides a good model and requires a relatively simple experiment. This is the advantage of the method of moments. In this paper, an analytical method based on the measurement of the process moments (characteristic areas) is proposed, to identify the five-parameter model (second-order process with zero plus time delay) from either the closed-loop or open-loop time responses of the process (in the time-domain), or the general-order transfer function with time delay (in the frequency-domain). The only parameter required by the user is the type of process (minimum phase or non-minimum phase process), which in practice can be easily determined from the time response of the process. The method can also be used to reduce the higher-order process model. The proposed identification method was tested on several illustrative examples, and compared to other identification methods. The comparison with existing methods showed the superiority of the proposed method. Moreover, the tests confirmed that the algorithm of the proposed method works properly for a wide family of process models, even in the presence of moderate process noise.

This paper presents an analysis of operational parameters of the commercial Organic Rankine Cycle (ORC) cogeneration unit integrated with biomass-fired boiler and municipal heating network. The analysis is based on field measurements in real operational conditions using standard industrial sensors installed within the system. Regression based mathematical modelling have been applied to develop robust predictive models of the ORC system for its diagnostics and production planning. Historical data collected within the Supervisory Control and Data Acquisition System of the plant have been used to establish correlations between key thermodynamic parameters. Results reveal off-design performance characteristics of the ORC unit and its individual components such as turbine, evaporator and condenser. There are also demonstrated results of application of the model for technical condition and performance monitoring, which can support decisions on maintenance activities. • Operational performance of biomass-fired ORC cogeneration plant is examined. • Regression based models of industrial ORC unit have been developed. • Empirical correlations were established for key system state parameters. • Performance characteristics of ORC unit is discussed. [ABSTRACT FROM AUTHOR]

The selective catalytic reduction (SCR) system, as one principal flue gas treatment method employed for the NOx emission control of the coal-fired power plant, is nonlinear and time-varying with great inertia and large time delay. It is difficult for the present SCR control system to achieve satisfactory performance with the traditional feedback and feedforward control strategies. Although some improved control strategies, such as the Smith predictor control and the model predictive control, have been proposed for this issue, a well-matched identification model is essentially required to realize a superior control of the SCR system. Industrial field experiment is an alternative way to identify the SCR system model in the coal-fired power plant. But it undesirably disturbs the operation system and is costly in time and manpower. In this paper, a process identification model of the SCR system is proposed and developed by applying the asymptotic method to the sufficiently excited data, selected from the original historical operation database of a 350 MW coal-fired power plant according to the condition number of the Fisher information matrix. Numerical simulations are carried out based on the practical historical operation data to evaluate the performance of the proposed model. Results show that the proposed model can efficiently achieve the process identification of the SCR system. [ABSTRACT FROM AUTHOR]

• Long term data related to H 2 S concentration in mining corridor has been considered. • Novel methodology for environmental data is proposed. • Daily pattern has been discovered in highly random data. • Method has been validated for real data measured in the deep mine. The quality assessment of the air in a deep underground mine is a challenging issue. It is a time-varying process and it depends on several factors, mainly on technological processes such as blasting, air conditioning, ventilation, machines operations, as well as gas released by rock mass and humidity. The air quality should be monitored and analyzed to understand the process as much as possible in order to facilitate the miner's work and to improve its safety. One of the most critical parameters of the air quality in the considered mine is the hydrogen sulphide ( H 2 S) concentration. It is related to the geology of deposit thus one should expect the random nature of the gas concentrations. In this paper, we focused on H 2 S concentration analysis based on long term monitoring. Signal segmentation procedure for raw data has been proposed, the segmented data (daily patterns) have been visualized and finally statistically analyzed. It has been found that there are deterministic components in the H 2 S data variation, which strongly depend on ventilation operation regimes. This can be a basis for further data analysis and for controlling air quality in the mine in a more effective way. [ABSTRACT FROM AUTHOR]

The article presents the issue related with a proper preparation of a data sheet for the analysis, the way of verifying the correctness and reliability of input information, and proper data encoding. Improper input or coding of data can significantly influence the correctness of performed analyses or extend their time. This stage of an analysis is presented by an authorship questionnaire for the study on occupational safety culture in a manufacturing plant, using the Statistica software for analyses. There were used real data, obtained during the research on the issue of occupational safety and factors having the greatest influence on the state of occupational safety. [ABSTRACT FROM AUTHOR]

In this article, data-driven models are developed for real time monitoring of sulfur dioxide and hydrogen sulfide in the tail gas stream of sulfur recovery unit (SRU). Statistical [partial least square (PLS), ridge regression (RR) and Gaussian process regression (GPR)] and soft computing models are constructed from plant data. The plant data were divided into training and validation sets using Kennard-Stone algorithm. All models are developed from the training data set. PLS model is designed using SIMPLS algorithm. Three different ridge parameter selection techniques are used to design the RR model. GPR model is designed using four hyper parameter selection methods. The soft computing models include fuzzy and neuro-fuzzy models. Prediction accuracy of all models is assessed by simulation with validation dataset. Simulation results show that the GPR model designed with marginal log likelihood maximization method has good prediction accuracy and outperforms the performance of all other models. The developed GPR model is also found to yield better prediction accuracy than some other models of the SRU proposed in the literature. [ABSTRACT FROM AUTHOR]

The paper offers an approach to the investigation of the dynamics of nonlinear non-stationary processes with the focus on the risk of dynamic system stability loss. The risk is assessed on the basis of the accumulated knowledge about power supply system operation. New methods for power supply modes analysis are developed and applied as follows: linear discrete point knowledge-based models are developed for nonlinear non-stationary objects; wavelet analysis is used for non-stationary processes; stability loss risks are analyzed through the investigation of spectral decompositions of Gramians of these linear predictive models. Case studies are included.

We have the main characteristics of moving as walking, running, sitting, dancing, eating, drinking and other activities. The range-of-motion, ROM, is the maximum number of movements that can occur in the sagittal, frontal, and transverse type and consists of flexion, extension, abduction, adduction, hyperextension and others. Decreased ROM can be the result of injury and the aging process and can lead to undesirable motion patterns. Feature extraction in motion analysis involve calculating a number of characteristic values independent of the size to produce the appropriate motion identification using the best method or algorithm during processing. Based on the data types obtained from the process of extracting properties collected using moment invariance, the results of identification based on ROM standard gave an accuracy of 98% using Back propagation Neural Network, 94% using Radial Basis Function Neural Network and 36% using K Means Clustering. [ABSTRACT FROM AUTHOR]

Industrial process status can be modified according to continuous operations, which are required by different production specifications. Commonly, in a multimode process, attention was always paid to stable modes, while transitions were neglected. In a transition process, the process may be externally time varying or nonstationary so that the identification and the modelling are intractable to implement using traditional statistical methods. In this article, a transition identification and process monitoring method is proposed to handle the above problems based on a novel dynamic mutual information similarity (DMIS) analysis. Firstly, a multimode process is represented by a series of overlapping moving windows to consider the local information. In order to extract the corresponding dynamic information, each of these windows is modelled using the dynamic partial least squares (DPLS) method. Then, the mutual information algorithm is introduced to calculate the similarity between different latent variables. The hierarchical clustering method is employed to transfer the similarity information into a visualized dendrogram where the whole process, including a transition, is divided into several segments. The statistical characteristics in each segment are relatively stable and can be characterized by conventional multivariate statistical process control methods. Online identification and fault detection are then carried out for the multimode process through a series of DPLS models, established in the offline steps. The feasibility and effectiveness of the proposed method are validated by the Tennessee Eastman (TE) benchmark and a real process. The results of the proposed methods have shown superior performance compared to previous works. [ABSTRACT FROM AUTHOR]