Order Process Model Research Articles

Unified design method of time delayed PI controller for first order plus dead-time process models with different dead-time to time constant ratio

Unified design method of time delayed PI controller for first order plus dead-time process models with different dead-time to time constant ratio

Process Query Language: Design, Implementation, and Evaluation

Organizations can benefit from the use of practices, techniques, and tools from the area of business process management. Through the focus on processes, they create process models that require management, including support for versioning, refactoring and querying. Querying thus far has primarily focused on structural properties of models rather than on exploiting behavioral properties capturing aspects of model execution. While the latter is more challenging, it is also more effective, especially when models are used for auditing or process automation. The focus of this paper is to overcome the challenges associated with behavioral querying of process models in order to unlock its benefits. The first challenge concerns determining decidability of the building blocks of the query language, which are the possible behavioral relations between process tasks. The second challenge concerns achieving acceptable performance of query evaluation. The evaluation of a query may require expensive checks in all process models, of which there may be thousands. In light of these challenges, this paper proposes a special-purpose programming language, namely Process Query Language (PQL) for behavioral querying of process model collections. The language relies on a set of behavioral predicates between process tasks, whose usefulness has been empirically evaluated with a pool of process model stakeholders. This study resulted in a selection of the predicates to be implemented in PQL, whose decidability has also been formally proven. The computational performance of the language has been extensively evaluated through a set of experiments against two large process model collections.

АЛГОРИТМ ВИМІРЮВАННЯ ПОДІБНОСТІ МІТОК ДІЯЛЬНОСТЕЙ НА ОСНОВІ NLP У БАЗІ ДАНИХ МОДЕЛЕЙ БІЗНЕС-ПРОЦЕСІВ

Business process modeling is an important part of organizational management since it enables companies to obtain insights into their operational workflows and find opportunities for development. However, evaluating and quantifying the similarity of multiple business process models can be difficult because these models frequently differ greatly in terms of structure and nomenclature. This study offers an approach that uses natural language processing techniques to evaluate the similarity of business process models in order to address this issue. The algorithm uses the activity labels given in the business process models as input to produce textual descriptions of the associated business processes. The algorithm includes various preprocessing stages to guarantee that the textual descriptions are correct and consistent. First, single words are retrieved and transformed to lower case from the resulting textual descriptions. After that, all non-alphabetic and stop words are removed from the retrieved words. The remaining words are then stemmed, which includes reducing them to their base form. The algorithm evaluates the similarity of distinct business process models using similarity measures, including Jaccard, Sorensen – Dice, overlap, and simple matching coefficients, after the textual descriptions have been prepared and preprocessed. These metrics provide a more detailed understanding of the similarities and differences across various business process models, which can then be used to influence decision-making and business process improvement initiatives. The software implementation of the proposed algorithm demonstrates its usage for similarity measurement in a database of business process models. Experiments show that the developed algorithm is 31% faster than a search based on the SQL LIKE clause and allows finding 18% more similar models in the business process model database.

Self-tuning kernel Gaussian method for predictive control systems

This paper introduces a novel Gaussian process approach for systems with unknown disturbances with quasi-periodic patterns to enhance the performance of a robust economic model predictive controller. A self-tuning kernel is proposed to substitute the periodic kernel used in the hybrid Gaussian process models in order to predict forthcoming disturbances more accurately while demanding a shorter training horizon. The proposed method is applied to a domestic solar thermal system, in which the hot water demand has a recurrent pattern, yet there is a slight variation daily. The simulation results demonstrate that the proposed controller has a reliable and precise prediction of future demand, and therefore it is able to manage the solar thermal system properly under various hot water load scenarios. Specifically, the proposed controller yields more than 5% improvement over a proportional–integral–derivative controller in terms of energy saving associated with the electricity usage for an auxiliary heat source.

Uncertainty analysis for techno-economic and life-cycle assessment of wet waste hydrothermal liquefaction with centralized upgrading to produce fuel blendstocks

Hydrothermal liquefaction is a promising technology that can produce fuels from waste with much lower greenhouse gas emissions and potential to supply 24% of total U.S. jet fuel demand. However, existing techno-economic and life-cycle assessment of hydrothermal liquefaction are subject to considerable uncertainties, which should be quantified to accelerate deployment. This work presents a comprehensive uncertainty analysis in both economic and environmental measures of the entire wet waste-to-fuel blendstock supply chain using the most recent developments in hydrothermal liquefaction and biocrude upgrading technologies. The results of a U.S. based analysis suggest an estimated biocrude yield from 42.2% to 52.4%, an estimated fuel yield from 34.7% to 42.7%, an estimated minimum fuel selling price from $2.28/gge to $3.45/gge, and an estimated supply chain greenhouse gas emissions reduction from 73.4% to 81.8% relative to petroleum-derived diesel. In addition, an open-source reduced order process model embedded with predictive reactor model is made available in the supplementary information, allowing communities interested in waste-to-energy to assess technology feasibility of hydrothermal liquefaction with their own regional data and track the technology development from economic, environment, and uncertainty perspectives. To the authors’ knowledge this model is the most comprehensive predictive model for hydrothermal liquefaction to date with regard to commercial relevancy (based on continuous testing of bench and engineering scale system), the number of data sets, and the breadth of wet wastes represented.

Modeling interconnections of safety and financial performance of nuclear power plants, part 3: Spatiotemporal probabilistic physics-of-failure analysis and its connection to safety and financial performance

This paper is a byproduct of a line of research by the authors to analyze interrelationships of safety and financial performance of nuclear power plants (NPPs). The result of this line of research is summarized in three parts: Part 1 covers a categorical review of relevant literature and the theoretical bases that support the methodological developments in Part 2. Part 2 introduces an Integrated Enterprise Risk Management (I-ERM) methodological framework to quantify the interconnections of safety and financial performance with a focus on operation and maintenance (O&M) of NPPs. Part 2 has also demonstrated the applicability and values of the I-ERM methodology through an NPP case study. This paper is Part 3, where detailed development and implementation of one of the I-ERM modules, i.e., probabilistic physics-of-failure (PPoF) analysis, and its connection with safety and financial performance is reported. In this article, the physical failure modeling for hardware components is advanced by incorporating finite element analysis (FEA) into PPoF analysis and coupling the FEA-based PPoF with the maintenance performance through a renewal process model. This article covers two scientific contributions: (i) first-of-its-kind incorporation of FEA into the PPoF model of thermal fatigue for NPP components; and (ii) advancing the interface between the PPoF analysis and the renewal process model in order to deal with spatiotemporal FEA outputs and to efficiently estimate the physical transition rates even when the PPoF outputs are dominated by success data. Through the incorporation of FEA, the resolution of the PPoF analysis is enhanced as spatiotemporal conditions such as stress and temperature can be considered explicitly instead of relying on simplified assumptions or analytical models with reduced spatiotemporal dimensions. To demonstrate an application of the FEA-based PPoF analysis and its coupling with maintenance through the renewal process model, a case study is conducted using excess letdown elbow piping in the chemical and volume control system of a Pressurized Water Reactor.

FOPI/FOPID Tuning Rule Based on a Fractional Order Model for the Process

This paper deals with the design of a control system based on fractional order models and fractional order proportional-integral-derivative (FOPID) controllers and fractional-order proportional-integral (FOPI) controllers. The controller design takes into account the trade-off between robustness and performance as well as the trade-off between the load disturbance rejection and set-point tracking tasks. The fractional order process model is able to represent an extensive range of dynamics, including over-damped and oscillatory behaviors and this simplifies the process modelling. The tuning of the FOPID and FOPI controllers is achieved by using an optimization, as a first step, and in a second step, several fitting functions were used to capture the behavior of the optimal parameters of the controllers. In this way, a new set of tuning rules called FOMCoRoT (Fractional Order Model and Controllers Robust Tuning) is obtained for both FOPID and FOPI controllers. Simulation examples show the effectiveness of the proposed control strategy based on fractional calculus.

Estimating a Non-parametric Memory Kernel for Mutually Exciting Point Processes

Abstract Self- and cross-excitation in point processes are commonly captured in the financial econometrics literature using a multivariate exponential memory kernel. In this article, the exponential assumption is relaxed and the resultant non-parametric memory kernel is estimated by a method based on second-order cumulants. The estimator is shown to be consistent and asymptotically normally distributed and performs well under simulation. An empirical application based on 10 international stock indices is presented. Two different indices of contagion between markets are constructed from the point process models in order to examine interconnection over time. A conclusion which emerges from these results is the assumption that a parametric kernel may be too restrictive as the application reveals interesting features, and in some cases substantial differences, between the exponential and non-parametric kernels.

Closed-loop identification of stable and unstable processes with time-delay

This article concerned with the parametric identification problem of continuous-time process dynamics with unknown time-delay. An improved closed-loop approach is developed to carryout process identification in terms of generalized first and second order plus time-delay (GFOPTD & GSOPTD) process models. These generalized models can include both stable and unstable plant dynamics. The key feature of the proposed method over existing techniques is its effective indirect nature, where the mathematical formulation is developed to identify continuous process dynamics and time-delay parameters, indirectly in terms of discrete parameters. The advantage of the proposed indirect method is that it not only allows to accurately identify process parameters but helps in achieving the single parameter based convergence, which is also proved mathematically. The identification accuracy and robustness of the proposed technique in the presence of measurement noise and modeling uncertainties is corroborated with the help of simulation examples on benchmark problems and by using a practical tank process experiment.

Demand response scheduling using derivative-based dynamic surrogate models

When assessing demand response to solve optimal scheduling problems, the optimization algorithm needs to be coupled with the process model in order to quantify the behavior of the monitored variable. For negligible transients, a first approximation consists of applying the steady state correlation between input and output variables. On the contrary, when dynamics show a relevant bias with respect to the estimated steady state response, a more accurate model is required. When coupling dynamic models of entire chemical processes with the optimization algorithm, the computational effort drastically increases. In those cases, the model should be simplified without losing its accuracy.In this research work, we propose a derivative-based approach for dynamic surrogate modeling applied to an ethylene oxide production scheduling problem. Thanks to its general validity, once derived, it can be applied to any setpoint trajectory. This approach allows to reduce the computational time of the optimization algorithm by orders of magnitude, providing results with the same accuracy as the detailed process simulation.

MPC Design for an Auditorium Building Using Data Driven Modeling

This paper describes the design of a model predictive controller (MPC) for preheating/-cooling of an auditorium building with a central air handling unit (AHU). The data-based approach uses system identification to estimate a low order nonlinear MPC model. The data used for identification is generated by simulation of a high order process model that was developed in Modelica using the open source Buildings library. This model is further used for validation of the predictive controller within Matlab/Simulink using Functional Mock-up Interface (FMI). Advantages of MPC compared to a conventional control strategy are simulatively proven using co-simulation, where an overall cost reduction of approx. 25% was achieved.

Effect of Delay Approximation of various orders on the open loop response of a Delayed Process Model

In present analysis, response of a selected delayed first order process model is compared for various orders of approximation of dead time present in the transfer function model. Effect of different orders of approximation of dead time is analysed on the open loop step response of the system in terms of percentage change in original and dead time approximated system models. For this purpose, consistency parameter in a headbox of a paper machine is selected as a process model as it closely approximates to a first order delayed process model.

Data-driven iterative tuning based active disturbance rejection control for FOPTD model

Active Disturbance Rejection Control (ADRC) emerges as a promising control method that can effectively handle uncertainties and disturbances. However, many model-based ADRC tuning methods turn laborious to achieve satisfactory control performance, when the critical process parameters are difficult to accurately obtain, especially the time delay information. To this end, this paper aims to propose a data-driven iterative tuning method for time-delayed ADRC (TD-ADRC). Based on parameter scaling technique, the quantitative correlation among control performance, robustness and normalized controller parameters are investigated. It is then used to design robust nominal controller. Then, based on the TD-ADRC inner-loop equivalent structure, an iterative feedback tuning (IFT) method is proposed to optimally obtain the nominal first order plus time delay (FOPTD) process model. Its unbiasedness and convergence are also described. With the empirical relations and the IFT stochastic approximation algorithm, a data-driven iterative tuning method for TA-ADRC is proposed, which allows a reasonable trade-off between system performance and robustness. Simulation results validate the efficacy of the proposed method, and a water-tank control experiment depicts a promising prospect in control practice.

An Experimental Approach towards Motion Modeling and Control of a Vehicle Transiting a Non-Newtonian Environment

The present work tackles the modeling of the motion dynamics of an object submerged in a non-Newtonian environment. The mathematical model is developed starting from already known Newtonian interactions between the submersible and the fluid. The obtained model is therefore altered through optimization techniques to describe non-Newtonian interactions on the motion of the vehicle by using real-life data regarding non-Newtonian influences on submerged thrusting. For the obtained non-Newtonian fractional order process model, a fractional order control approach is employed to sway the submerged object’s position inside the viscoelastic environment. The presented modeling and control methodologies are solidified by real-life experimental data used to validate the veracity of the presented concepts. The robustness of the control strategy is experimentally validated on both Newtonian and non-Newtonian environments.

Enhancing an Intelligent Digital Twin with a Self-organized Reconfiguration Management based on Adaptive Process Models

Shorter product life cycles and increasing individualization of production leads to an increased reconfiguration demand in the domain of industrial automation systems, which will be dominated by cyber-physical production systems in the future. In constantly changing systems, however, not all configuration alternatives of the almost infinite state space are fully understood. Thus, certain configurations can lead to process instability, a reduction in quality or machine failures. Therefore, this paper presents an approach that enhances an intelligent Digital Twin with a self-organized reconfiguration management based on adaptive process models in order to find optimized configurations more comprehensively.

Coarsening in algebraic multigrid using Gaussian processes

Multigrid methods have proven to be an invaluable tool to efficiently solve large sparse linear systems arising in the discretization of Partial Differential Equations (PDEs). Algebraic multigrid methods and in particular adaptive algebraic multigrid approaches have shown that multigrid efficiency can be obtained without having to resort to properties of the PDE. Yet the required setup of these methods poses a not negligible overhead cost. Methods from machine learning have attracted attention to streamline processes based on statistical models being trained on the available data. Interpreting algebraically smooth error as an instance of a Gaussian process, we develop a new, data driven approach to construct adaptive algebraic multigrid methods. Based on Gaussian a priori distributions, kriging interpolation minimizes the mean squared error of the a posteriori distribution, given the data on the coarse grid. Going one step further, we exploit the quantification of uncertainty in the Gaussian process model in order to construct efficient variable splittings. Using a semivariogram fit of a suitable covariance model we demonstrate that our approach yields efficient methods using a single algebraically smooth vector.

A Modeling Method for Model-Driven API Management

This article reports on the Design Science engineering cycle for implementing a modeling method to support model-driven, process-centric API management. The BPMN standard was hereby enriched on semantic, syntactic and tool levels in order to provide a viable solution for integrating API requests with diagrammatic business process models in order to facilitate the documentation or testing of REST API calls directly in a modeling environment. The method can be implemented by stakeholders that need to map and manage their API ecosystem, thus gaining more API management agility and improving their software engineering productivity. By assimilating API ecosystem conceptualization in the modeling environment, the proposal differs from both RPA (which typically employs non-BPMN process diagramming e.g., in UIPath) and BPM Systems (which typically isolate all API-related semantics outside the process modeling language to keep the diagrammatic representation standard-compliant).

PFLOTRAN-SIP: A PFLOTRAN Module for Simulating Spectral-Induced Polarization of Electrical Impedance Data

Spectral induced polarization (SIP) is a non-intrusive geophysical method that collects chargeability information (the ability of a material to retain charge) in the time domain or its phase shift in the frequency domain. Although SIP is a temporal method, it cannot measure the dynamics of flow and solute/species transport in the subsurface over long times (i.e., 10–100 s of years). Data collected with the SIP technique need to be coupled with fluid flow and reactive-transport models in order to capture long-term dynamics. To address this challenge, PFLOTRAN-SIP was built to couple SIP data to fluid flow and solute transport processes. Specifically, this framework couples the subsurface flow and transport simulator PFLOTRAN and geoelectrical simulator E4D without sacrificing computational performance. PFLOTRAN solves the coupled flow and solute-transport process models in order to estimate solute concentrations, which were used in Archie’s model to compute bulk electrical conductivities at near-zero frequency. These bulk electrical conductivities were modified while using the Cole–Cole model to account for frequency dependence. Using the estimated frequency-dependent bulk conductivities, E4D simulated the real and complex electrical potential signals for selected frequencies for SIP. These frequency-dependent bulk conductivities contain information that is relevant to geochemical changes in the system. This study demonstrated that the PFLOTRAN-SIP framework is able to detect the presence of a tracer in the subsurface. SIP offers a significant benefit over ERT in the form of greater information content. It provided multiple datasets at different frequencies that better constrained the tracer distribution in the subsurface. Consequently, this framework allows for practitioners of environmental hydrogeophysics and biogeophysics to monitor the subsurface with improved resolution.

Identifying user assistance systems for radiotherapy to increase efficiency and help saving lives

ABSTRACT Increasing efficiency and reducing risk in radiotherapy cancer treatment is of high importance. User assistance systems within a digitally connected radiotherapy environment can support all involved professionals to perform their individual tasks faster and better. This paper presents a qualitative analysis of radiotherapy workflows and a corresponding process modelling in order to identify hypothetical user assistance systems for specific process activities. In addition, the results of an empirical study on the identified systems are presented together with derived requirements and design principles for these systems. A structured online survey with 50 medical physicists in Germany has been conducted. Among others the acceptance, the increase of perceived efficiency and the risk reduction while using the assistance systems are analysed and discussed. The results support the creation of value adding user assistance systems for radiotherapy that improve efficiency, reduce treatment risks and reach high user acceptance levels.

SMDP-based sleep policy for base stations in heterogeneous cellular networks

A dense heterogeneous cellular network can effectively increase the system capacity and enhance the network coverage. It is a key technology for the new generation of the mobile communication system. The dense deployment of small base stations not only improves the quality of network service, but also brings about a significant increase in network energy consumption. This paper mainly studies the energy efficiency optimization of the Macro-Femto heterogeneous cellular network. Considering the dynamic random changes of the access users in the network, the sleep process of the Femto Base Stations (FBSs) is modeled as a Semi-Markov Decision Process (SMDP) model in order to save the network energy consumption. And further, this paper gives the dynamic sleep algorithm of the FBS based on the value iteration. The simulation results show that the proposed SMDP-based adaptive sleep strategy of the FBS can effectively reduce the network energy consumption.