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Accurate identification and modeling of process maps in additive manufacturing remains a pertinent challenge. To ensure high quality and reliability of the finished product researchers, rely on models that entail the physics of the process as a computer code or conduct laboratory experiments, which are expensive and oftentimes demand significant logistic and overheads. Physics-based computational modeling has shown promise in alleviating the aforementioned challenge, albeit with limitations like physical approximations, model-form uncertainty, and limited experimental data. This calls for modeling methods that can combine limited experimental and simulation data in a computationally efficient manner, in order to achieve the desired properties in the manufactured parts. In this paper, we focus on demonstrating the impact of probabilistic modeling and uncertainty quantification on powder-bed fusion (PBF) additive manufacturing by focusing on the following three milieu: (a) accelerating the parameter development processes associated with laser powder bed fusion additive manufacturing process of metals, (b) quantifying uncertainty and identifying missing physical correlations in the computational model, and (c) transferring learned process maps from a source to a target process. These tasks demonstrate the application of multifidelity modeling, global sensitivity analysis, intelligent design of experiments, and deep transfer learning for a meso-scale meltpool model of the additive manufacturing process.

OBJECTIVEIncidental durotomy is a common complication of elective lumbar spine surgery seen in up to 11% of cases. Prior studies have suggested patient age and body habitus along with a history of prior surgery as being associated with an increased risk of dural tear. To date, no calculator has been developed for quantifying risk. Here, the authors’ aim was to identify independent predictors of incidental durotomy, present a novel predictive calculator, and externally validate a novel method to identify incidental durotomies using natural language processing (NLP).METHODSThe authors retrospectively reviewed all patients who underwent elective lumbar spine procedures at a tertiary academic hospital for degenerative pathologies between July 2016 and November 2018. Data were collected regarding surgical details, patient demographic information, and patient medical comorbidities. The primary outcome was incidental durotomy, which was identified both through manual extraction and the NLP algorithm. Multivariable logistic regression was used to identify independent predictors of incidental durotomy. Bootstrapping was then employed to estimate optimism in the model, which was corrected for; this model was converted to a calculator and deployed online.RESULTSOf the 1279 elective lumbar surgery patients included in this study, incidental durotomy occurred in 108 (8.4%). Risk factors for incidental durotomy on multivariable logistic regression were increased surgical duration, older age, revision versus index surgery, and case starts after 4 pm. This model had an area under curve (AUC) of 0.73 in predicting incidental durotomies. The previously established NLP method was used to identify cases of incidental durotomy, of which it demonstrated excellent discrimination (AUC 0.97).CONCLUSIONSUsing multivariable analysis, the authors found that increased surgical duration, older patient age, cases started after 4 pm, and a history of prior spine surgery are all independent positive predictors of incidental durotomy in patients undergoing elective lumbar surgery. Additionally, the authors put forth the first version of a clinical calculator for durotomy risk that could be used prospectively by spine surgeons when counseling patients about their surgical risk. Lastly, the authors presented an external validation of an NLP algorithm used to identify incidental durotomies through the review of free-text operative notes. The authors believe that these tools can aid clinicians and researchers in their efforts to prevent this costly complication in spine surgery.

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.

Industrial data sets are often contaminated with outliers due to sensor malfunctions, signal interference, and other disturbances as well as interplay of various other factors. The effect of data abnormalities due to the outliers has to be systematically accounted while developing models that are resistant towards unforeseen effects of the outliers. The spectrum of methods that account for irregularities in process data while modeling are collectively known as robust identification methods. Even though, there are various non-probabilistic methods to tackle robust identification, few of them have considered the effect of outliers explicitly. In contrast to that, probabilistic identification methods ensure that these effects are given due attention. Despite these advantages, the probabilistic robust identification strategies have hardly been explored by practitioners. This review paper provides a general introduction to the probabilistic methods for robust identification, illustrates the main steps involved in the development of models, and reviews the related literature. Further, the paper contains two tutorial examples, including an industrial case study, to highlight various steps involved in the robust identification process.

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...

The spread of opioid crisis has posed many negative impacts on the society of the United State and attracts the attention of the US government and many relevant organizations to mitigate the increasing of drug abuse. This passage mainly describe the spreading trend of opioid crisis between five states/counties and analyze the important social and economic data of America to identify some important influential factors. In this way, an optimized spreading model based on cellular automata with the combination of life-cycle graph of the four stages in spreading process can be achieved and the exact starting point of each state can be known. With the factor analyze of socio-economic information extracted from the U.S. Census Bureau,the characteristics and idiosyncrasy of people who commonly get drug abusing problems can be identified And with more factors taken into consideration the spreading model can be optimized with multi-layer factors so that more strategies can be made to help restricting the drug abuse.

The quality of the air in underground mines is a challenging issue due to many factors, such as technological processes related to the work of miners (blasting, air conditioning, and ventilation), gas release by the rock mass and geometry of mine corridors. However, to allow miners to start their work, it is crucial to determine the quality of the air. One of the most critical parameters of the air quality is the carbon monoxide (CO) concentration. Thus, in this paper, we analyze the time series describing CO concentration. Firstly, the signal segmentation is proposed, then segmented data (daily patterns) is visualized and statistically analyzed. The method for blasting moment localization, with no prior knowledge, has been presented. It has been found that daily patterns differ and CO concentration values reach a safe level at a different time after blasting. The waiting time to achieve the safe level after blasting moment (with a certain probability) has been calculated based on the historical data. The knowledge about the nature of the CO variability and sources of a high CO concentration can be helpful in creating forecasting models, as well as while planning mining activities.

During surgery for brain tumor, expert surgeons consider maximum brain tumor removal and minimum postoperative neuroglial complications. At this brain tumor resection, the surgeon resects based on his knowledge and experience, the surgical process, work contents, and duration of surgery vary by cases. It is difficult for the young surgeons and surgical staffs to understand the surgical process involved. Visualization of the surgical process is an effective tool for aiding the understanding among the surgical staff. This paper presents a surgical identification system using intra-operative information and machine learning. We extract surgical process features using navigation system's log, MR images and microscope video. Then, the surgical processes are identified using Hierarchical Hidden Markov Model. The method has been evaluated using past log data (navigation system's log, MR images and microscope video). The accuracy of the identified surgical processes was 84% in 12 processes. This result indicated that the surgical process identification error is a few minutes, high revel accuracy. In addition, this result is a possibility to support understanding of surgical processes for young surgeons and surgical staffs.

The article presents the results of analysis, the use of continuous flow of logic at the stage of production planning and control of the company producing furniture. The concept of continuous flow tends to regulate the flow of materials in a manner that provides the shortest flow path without unnecessary activities (Muda is a Japanese word meaning waste), a constant takt and defined throughput at constant resource requirements for the so-called transfer of material through the whole process. In the study Glenday’d sieve method was used to identify the correct area, which requires the value stream mapping, and areas called excessive complexity, which do not provide added value. The use of Glenday’s sieve method made it possible to identify areas in which it must be improve production capacity.

Background A significant portion of each country's budget is assigned to its health care system annually. Given the current economic conditions, saving costs is a key issue. Therefore, any situation that leads to lower efficiency and higher costs should be remedied. With much of the hospitals' budgets spent on operating rooms, it is important to use strategies to increase their efficiency and quality. The purpose of this study is to design a mechanism to reduce the cancellation of elective surgical procedures, which incurs considerable costs. Methods To this end, the data gathered from an educational hospital in Tehran were evaluated. The reasons for the surgical cancellations were determined through data analysis and process identification. Subsequently, considering the contents of the current literature and consultations with the hospital staff, suggestions were made to reduce surgical cancellations. Finally, using a solution allocation model, the best solution in terms of costs, efficiency, and implementation time was assigned by using the Fuzzy Extended Analytical Hierarchy Process to each of the reasons and surgeries. Results By solving the allocation model using the GAMs software, the solutions were assigned to specific reasons and surgeries. This solved model assigns the best solutions to cancellation reasons regarding solutions cost, efficiency and implementation time. Conclusions The cancellation of the six investigated surgeries alone imposes on the hospital approximately 1165,653,450 rials annually, so it is true to assume that, in general, reducing the cancellation of surgery can reduce hospital costs considerably.

Wood is one of Indonesia’s very rich natural resources abundant because the number reaches around 4,000 species. The process of identifying wood species currently it is still done manually in a relatively long time by observing types of fibers, vessels, rays, and other structures directly because there is not a much automatic application of identification of wood species is made. This is an obstacle for experts anatomy of wood because it must check wood species accurately and quickly. Therefore that, the field of Computer Vision is the right solution to develop the process Identification of wood species automatically. In this research program will be made application of Computer Vision to identify wood species with using the Daubechies Wavelet (DW) and Local Binary Pattern (LBP) methods for The extraction of the wood pattern is then classified Support Vector Machine (SVM) method. Results obtained in this study is able to identify the microscopic image of wood as a species of wood with average SVM accuracy is 85%.

Process reengineering (PR) in manufacturing organizations is a big challenge, as shown by the high rate of failure. This research investigated different approaches to process reengineering to identify limitations and propose a new strategy to increase the success rate. The proposed methodology integrates data as a procedure for process identification (PI) and mapping and incorporates process verification to analyze the changes made in a specific process. The study identifies interdependency within the manufacturing process (MP) and proposes a generic process reengineering approach that uses simulation and analysis of production line data as a method for understanding the changes required to optimize the process. The paper discusses the methodology implementation technique as well as process identification and the process mapping technique using simulation tools. It provides an improved data-driven process reengineering framework that incorporates process verification. Based on the proposed model, the study investigates a production line process using the WITNESS Horizon 21 simulation package and analyse the efficiency of data-driven process reengineering and process verification in terms of implementing changes.

When very detailed simulation of geometrically complex objects is needed, finite element models are often the best choice due to their description of spatial and temporal behavior of the modelled object. The drawback of finite element models is their large number of parameters. This makes it difficult to match a model to a real measured object by process identification methods. In this paper this problem is addressed by new software that is compatible to ANSYS as the probably most known finite element software.

This paper considers the problem of estimating the frequency response of an unknown plant from impulse or step response measurements, possibly with non-ideal step signals. The proposed method is direct no model identification of the plant is needed and it can be applied using a single set of data generated by the plant, with no need for specific experiments nor iterations. The frequency response of the plant, at a desired frequency, is estimated by a virtual experiment generating a filtered version of the data set. Numerical simulations show the effectiveness of the method.

Spatial spread of infectious diseases among populations via the mobility of humans is highly stochastic and heterogeneous. Accurate forecast/mining of the spread process is often hard to be achieved by using statistical or mechanical models. Here we propose a new reverse problem, which aims to identify the stochastically spatial spread process itself from observable information regarding the arrival history of infectious cases in each subpopulation. We solved the problem by developing an efficient optimization algorithm based on dynamical programming, which comprises three procedures: i, anatomizing the whole spread process among all subpopulations into disjoint componential patches; ii, inferring the most probable invasion pathways underlying each patch via maximum likelihood estimation; iii, recovering the whole process by assembling the invasion pathways in each patch iteratively, without burdens in parameter calibrations and computer simulations. Based on the entropy theory, we introduced an identifiability measure to assess the difficulty level that an invasion pathway can be identified. Results on both artificial and empirical metapopulation networks show the robust performance in identifying actual invasion pathways driving pandemic spread., 14pages, 8 figures; Accepted by IEEE Transactions on Cybernetics

For many decades, process models have built the basis for economically successful participation in the market. Companies are still faced with the task of identifying, defining and visualizing their processes, especially in today’s era of digitization. In this era and due to more and more complex inter-organizational processes across corporate boundaries, the question arises as whether techniques and approaches for elicitation of processes in the context of business process management have changed or if the established techniques are still appropriate. Here, digitization could have significant potential to automate the elicitation of processes. To address this issue we have conducted a systematic literature review and identified the theoretical requirements for the elicitation of processes. Then, based on an interview study with experienced consultants, we compared the theoretic results with the current applied techniques in today’s practice. Selected results are presented and discussed in this paper.

Item does not contain fulltext Employing nurses as opinion leaders to implement guidelines may be a promising implementation activity. Until now, insight into necessary competencies of nurse opinion leaders is lacking. We studied and supported aspiring nurse opinion leaders, using a training program based on social influence and implementation theory. Twenty-one competencies were identified, of which the most important were cooperating, communicating, delegating, giving feedback, networking, and information processing. Understanding and addressing these competencies may support the implementation of evidence-based guidelines.

This paper presents a software tool developed for aiding the identification process of dynamic systems. This first version of the tool allows the user to generate excitation signals that are suitable for each kind of plant and also to automatically treat the raw process data in order to identify different types of models, which may include multiple-input and multiple-output (MIMO) and nonlinear behaviors. The potential of the tool is illustrated by performing the identification process of an industrial test stand for the energetic performance evaluation of refrigerant compressors. The results compare linear and nonlinear models for the process around an operating point and show that the proposed tool provides good results even for this nontrivial MIMO process, which presents dead times and other nonlinearities.

Model Predictive Control (MPC) Relevant Identification (MRI) methods are a good option for identification, if there is model structure mismatch. Herein a new MRI method, named Enhanced Multistep Prediction Error Method (EMPEM), is proposed. EMPEM combines the best characteristics of others MRI methods in a single algorithm. It was developed to identify either closed-loop or open-loop systems; its convergence and stability make it perform better than the other presented methods. To show the advantages of EMPEM, a comparison is made against two other methods (one MRI and one PEM). The statistical analysis indicates that in the cases studied, the performance and the robustness of the new method is equal or better than the other ones.

The paper describes a mathematical model of PHA biopolymer production process by Pseudomonas putida KT2442 where the octanoic acid is used as a substrate. The process is modeled using mass balances for fed-batch cultivation. Proper fitting to experimental data is obtained by identification of the model parameters. The model exhibits good agreement with experiments and its possible application for control is considered in the paper.

Changes in human capital management, new requirements regarding knowledge and skills of employees compel higher education institutions to redefine their learning programmes. This requires evaluation of the didactic process realization, which should be oriented on competences. In the article authors presents an approach to competence modeling. New tools and collaboration mechanisms are proposed, which allow defining the structure of competence, analyzing the level of competence development, and assessing the competence process realization in relation “expected benefit-required expense”.

This study presents a new method to identify parameters representing cutting process and transfer function of flexible mechanical structures mounted on a traveling stage by utilizing only internal information of computerized numerical control (CNC) system. Disturbance force input to CNC is estimated by disturbance observer and cutting force is estimated based on cutting force model. Analyzing influence of the estimated cutting force on the disturbance force, parameters used in the assumed models of cutting process and structural dynamics are identified in quasi-real-time. Least square method (LSM) is utilized for the parameter identification. Face turning experiment using an ultra-precision machine tool was conducted to verify feasibility of the proposed method. Experimental results clarified that the cutting force coefficient and the modal parameters representing the dynamic characteristics of the force transfer function can be identified accurately by the proposed method.

The national green building evaluation is divided into two phases of design and operation label. Because of involving many units, complicated operation and long construction, it is difficult to achieve the whole process identification of green building.It refines the assessment process of green building into conceptual design, predesign, construction drawing stage, green construction, completion and acceptance of construction, operation and maintenance respectively.Furthermore, the detail requirements and implementary strategies are expatiated in the article, combined with related theory and engineering design. Additional, it analyzed on green strategies of evaluation based on an actual engineering to provide a reference for green building assessment.

The bias derivative method for aeroengine’s State Variable Model(SVM) doesn’t have a satisfying accuracy. This method usually needs a linear modification to achieve a higher accuracy. In order to obtain a SVM with a good accuracy, this paper proposes a process identification based method. In this method, the coefficient matrices of the SVM are identified based on the input and output of the nonlinear model, according to the principle that the step responses of the SVM and the nonlinear model should be consistent. Then, the SVMs of small perturbation about steady operating points of an engine are established. Simulation results show that the SVMs established by the process identification based method have a good fidelity both in terms of steady and dynamic performance.

Complex environmental models are controlled by a high number of parameters. Accurately estimating the values of all these parameters is almost impossible. Sensitivity analysis (SA) results enable the selection of the parameters to include in a calibration procedure, but can also assist in the identification of the model processes. Additionally, a sensitivity analysis can yield crucial information on the use and meaning of the model parameters. This paper presents a Sobol' sensitivity analysis for flow simulations by a SWAT model of the river Kleine Nete, with the objective to assess the first order, second order and total sensitivity effects. Confidence intervals for the resulting sensitivity indices are inferred by applying bootstrapping. The results indicate that the curve number value (CN2) is the most important parameter of the model and that no more than 9 parameters (out of 26) are needed to have an adequate representation of the model variability. The convergence of the parameter ranking for total sensitivity effects is relatively fast, which is promising for factor fixing purposes. It is also shown that the Sobol' sensitivity analysis enhances the understanding of the model, by e.g. pointing out 3 significant pairwise interactions. In general, it can be concluded that the Sobol' sensitivity analysis can be successfully applied for factor fixing and factor prioritization with respect to the input parameters of a SWAT model, even with a limited number of model evaluations. The analysis also supports the identification of model processes, parameter values and parameter interaction effects.

A pure thermal plume development arising from a finite-size rotating heat source was analyzed experimentally. Qualitative investigation through extensive visualization has brought into focus the existence of a threshold rotation frequency (i.e., a swirl number) above which stretching effects are strengthened, thereby forcing the ascending plume motion to spiral around the geometrical axis heat source. Nevertheless, above the threshold frequency (i.e., above the swirl number), unstable processes appear through flow field pulsation in close proximity to the heat source; the pulsations literally beat and drive the flow field vicinity. From a strictly quantitative point of view, the data underscore the fact that heat source rotation presents two opposed trends. Below the threshold frequency, the higher the frequency, the more the temperature level is concentrated on the plume axis. In contrast, at the strongest rotation frequencies studied, the opposite is observed. Above the threshold rotating frequency, the...

Soft sensors for distillation end point (D95) on-line estimation in crude distillation unit (CDU) are developed. Experimental data are acquired from the refinery distributed control system (DCS) and include on-line available continuously measured variables and laboratory assays. Soft sensors are developed using different linear and nonlinear identification methods. Additional laboratory data for model identification are generated by Multivariate Adaptive Regression Splines (MARSplines). The models are evaluated based on Route Mean Square (RMS), Absolute Error (AE), FIT and Final Prediction Error (FPE) criteria. The best results are achieved with Box Jenkins (BJ), Output Error (OE) and Hammerstein–Wiener (HW) model. Based on developed soft sensors it is possible to estimate fuel properties in continuous manner and apply inferential control. By real plant application of developed soft sensors considerable savings could be expected, as well as compliance with strict law regulations for product quality specifications.

This paper deals with individualizing the surface-hardening vacuum carburizing process based on optimal process control. The authors' parametric identification determined that the mathematical model for the vacuum carburizing process requires the use of different mass-transfer coefficients at the stages of saturation and diffusion. The authors analyzed the effect that the optimal surface-hardening process conditions have on the structures and properties of drill-bit steels.

The paper discusses smart soft sensor design methods for industrial processes. The approach proposed is based on virtual models and associative search techniques. Fuzzy model is applied in combination with production knowledgebase to compensate for the lack of lab data. Fuzzy specification of certain process variables is practiced.

In the paper the mathematical modeling methods were employed to solve the problem of fuel mass estimation supplied to the power generating boiler grid. It was shown that popular polynomial and neural network based identification algorithms can be used to measure amount of fuel supplied to the boiler combustion chamber. Furthermore the original conception and exemplary realization of the mass stream measuring system was presented.

The paper describes some of the recent developments and applications for knowledge extraction from trained artificial neural network based hydrological models. The methods are in general derived from the casual relationship between inputs and outputs, and analysis of the hidden neuron behaviour. The discussions are focused on works related to explaining the internal behaviour and embedded physical process identification in a trained ANN rainfall-runoff model. It is envisaged that knowledge extraction is important for artificial neural networks to gain wider degree of acceptance especially in the context of hydrologic modeling. Therefore, this domain has to become a major field of research since it validates the use of neural networks for applications where reasons or explanations on why or how a result has been achieved are important.

The strategy for the collection of information (i.e., data) for model development is called experimental design. Optimal design seeks to maximize the information content under constraints of time and sampling. In the system identification literature the dominant strategy has been the method of pseudo random sequences (PRS). However, this work demonstrates that statistical design of experiments (SDOE) can provide greater information content as quantitatively measured by the D-optimal criterion.

The subject of the current paper is the collaborative practices used in the product development process in SMEs (small and medium enterprises). The starting point is an empirical study, part of industry-based fieldwork on the introduction of a product lifecycle management (PLM) system. Our results highlight the need for new approaches to take into account the socio-technical complexity of the collaborative processes. A new tool named CoCa is proposed to analyse collaborative practices in situ. This tool is designed to be used by researchers, consultants or, eventually, project managers in order to track all the collaborative events and the project context. The background and industrial case study, the theoretical basis and design of the tool are described and, finally, some indication is given of its potential use in gaining understanding of complex collaborative processes and in improving design coordination.

This paper presents a novel method to generate surface dynamometer cards of a sucker rod pump by applying torque and rotational speed estimates of a variable speed induction motor drive and a pumping process identification run.

In this work the identification of first-order plus dead-time models from a relay experiment is considered. The relay excitation is applied to the closed-loop. Alternative techniques for identification are examined and simple algorithms are proposed for dealing with the dead-time. Simulation examples are used to illustrate the techniques.

A procedure for surveying process data sets is presented. For this, linear models constructed in varying length, sliding data windows to determine the usefulness of data segments for process identification are utilised. The discussed approach has been applied to an industrial wood debarking plant and a biomass boiler analysis, enabling the preliminary study of process variables and conditions affecting the non-optimal process conditions. In addition, main process interactions and delays were easily discovered from the structures of the interpretable linear model candidates. It is concluded that the analysis can provide valuable information also for modelling and control of continuous processes.

Even though FOPDT(First-Order Plus Dead-Time) process is most widely applied in the industrial control field, it is difficult to figure out a in precise process model because of the long dead-time problem. Also, control performance may be deteriorated due to the mismatch problem of plant and model. Thus, the accuracy of process identification is the most important problem in FOPDT process control. In this paper, the proposed method using real-coded genetic algorithm outperforms the existing estimation methods that use step-test responses. The proposed strategy obtained useful result through a number of simulation examples.

This paper introduces industrial application of model predictive control (MPC) for the series of columns in a linear alkylbenzene (LAB) complex. The APC system that is consisted of twelve controlled variables, twelve manipulated variables and eight disturbance variables is used to deal with the constrained multivariable control problem of the distillation columns. Firstly, process modelling that includes experimental test and process identification is presented. Then, the construction and implementation of the APC system for the distillation columns are discussed. Industrial application results show that the APC system can maintain the best operation for a long time and realize ultimate operating potential of the distillation columns.

Most industrial processes are nonlinear. In such a case only a nonlinear model valid for the whole working area can ensure a good controller design. The nonlinear process is approximated by a multi-model consisting of the intelligent combination of some linear sub-models. As a very practical way the following identification strategy was used: independent model parameter estimation in the different working points and the calculation of the global valid model output as the weighted sum of the sub-models. As a weighting function the Gaussian function is used. The parameters of the Gaussian function were chosen either without or with optimization of the identification cost function. The global valid nonlinear model was used for model based predictive control. A heat exchanger example illustrates the method.

This paper presents a quantitative analysis of the model order selection problem, and its application for system identification of an ethylene furnace with open-loop and closed-loop industrial plant data. Empirical ARX models are used to describe the physical phenomena in the ethylene furnace. Appropriate model order selection is done based on the information content in the industrial data from the ethylene plant. Model order is chosen by using Akaike's information criterion (AIC), Rissanen's minimum description length (MDL), and a criterion based on the unmodeled output variation (UOV). UOV results in a smaller order model that has well-defined parameters with tight confidence intervals as compared to AIC and MDL. Similar models are obtained using closed-loop and open-loop data from the industrial process when UOV is used because the models are well-determined.

The paper presents a tool for process identification, controller tuning and control circuit simulation based on spreadsheets like MS-Excel. Process identification and modelling can be carried out for the open and closed-loop control circuit. Controller tuning is done according to the criterion of cascaded damping ratios. The design is based on a direct relation between the parameters of the process and the controller. Tuning for optimal set-point control as well mdisturbance rejection is provided. Single-input/single output (SISO) and dual-input/dual-output (DIDO) systems can been simulated for proportional and integral plants with dead-time.

An interactive intelligent software environment MULTI-EDIP for computer aided signal and system identification is presented. Motivation for the development of intelligent software for process identification is discussed. A summary of services offered by MULTI-EDIP and its main features are presented, particularly the problem of expert advice in model structure determination is highlighted. An example of intelligent support in electro-acoustic plant identification for active noise control application is described.

To support modern control methods on Motorola 68HC11 microcomputer several program modules were created as a part of program library. The created modules implement some algorithms from the province of adaptive and robust control. A module for recursive process identification was created as well as two modules for controller synthesis, which use the pole placement and the inversion of dynamic methods. A simple robust controller was also designed. Several applications were developed using both new and older modules to verify that the new code is fully functional and compatible with the library. Experiments proved that it is possible to employ modern methods of automatic control on existing hardware and achieve the benefits these methods offer, such as simple utilization, better quality of the control or energy savings.

It is difficult to identify some chemical processes which are processed in the complex environments and their operation conditions are frequently modified. In this paper we combine two effective identification tools, multiple models and neural networks and suggest a new identification approach. Hysteresis switch algorithm is applied, in order to select the best model of a set neuro identifiers at each time. The convergence the multiple neuro identifiers is proven. Simulation results show that the multiple neuro identifiers have better performance than those the individual neuro identifier for the pH neutralization and the fermentation process.

A refined relay feedback identification autotuning PID/PI is proposed in this paper which is capable of controlling long time-delay processes. The process is approximated via FOPDT or SOPDT models, whose parameters are determined through a modified relay feedback identification method. Employing zero-pole cancellation principle, the PID/PI is tuned by the specified amplitude and phase margins, which can guarantee fast response and strong robustness to the closed loop system. Model identification and controller parameter tuning are done on-line without much influence on the normal operation. In addition, this algorithm also has good disturbance rejection capability.