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The impact of the length of axially segmented lifters, as used in industrial rotary drums, on their process behavior and in particular the axial solids transport is analyzed in this study. Experime...

Wastewater sludge from a poultry slaughterhouse treatment plant was recovered through conversion into biodiesel by ultrasound-assisted in situ transesterification. The main effects of the process parameters were investigated at three levels, and their empirical relationship was modeled using artificial neural network (ANN) and response surface methodology (RSM). The developed models predicted the process behavior with excellent accuracy. Although both models had similar prediction performances, ANN marginally outperformed RSM. The capability of the genetic algorithm (GA) combined with the RSM (RSM-GA) and ANN (ANN-GA) models was evaluated for optimizing the process variables. The maximum biodiesel yield (21.45% w/w) was obtained using the ANN-GA model under optimized conditions, i.e., at the reaction time of 39.69 min, H2SO4 concentration of 3.34% (v/v), methanol-to-sludge relative content of 14.91:1 (mL/g), and ultrasound power of 104.87 W. Consequently, a combination of ANN and GA was proposed to model and optimize the transesterification process. The biodiesel yield obtained in this study was higher than the previously reported values from tannery (10.98%), dairy (13.46%), and municipal (18.58%) sewage sludge. This study specified biodiesel with a fatty acid methyl esters content of 96.86% using gas chromatography-mass spectrometry, Fourier transform infrared, and nuclear magnetic resonance spectroscopy.

A comprehensive analysis of the quality parameters of fused-cast aluminum-zirconium-silicate (AZS) refractories for glass furnaces has been carried out. It is shown that the assessment of the quality of AZS refractories by the content of ZrO2 and density in them does not give an objective idea of their operational properties. Of fundamental importance are the chemical composition and behavior of the glass phase, which determine the volume and temperature of the onset of exudation. Among the most important conditions for obtaining high-quality AZS refractories, characterized by a melting volume of 2‒3 % of the glass phase and a melting start temperature above 1400 °C, include the oxidative melting technology and the content of impurities in the chemical composition of the refractory no more than 0,25‒0,30 %. The conditions for the service of AZS refractories in the melting basin and the working space of glass-melting furnaces are formulated. Their influence on the course of the exudation process, the corrosion resistance of refractories and the formation of defects in glass is shown. Ill. 2. Ref. 30. Tab. 4.

Statistical evidence is presented to answer the title question using graphical tools from process behavior charting as well as ranking methods based on principal component analysis. These tools pro...

Digital manufacturing is one of the enabling methodologies associated with the concept of flexible mass customization, which has been boosted by the recent advances in Additive Manufacturing (AM) technologies, from which complex freeform geometries, internal and reentrant features can be reproduced based on the CAD model. From the standpoint of coordinate metrology, AM calls for a measurement technology capable of acquiring high data density of these complex structures. Computed Tomography (CT) is the technology of choice to holistically measure them and respond to the needs of mass customization. However, due to the complexity of the CT measurement chain, the numerous influence factors, and their difficult quantification, the measurement uncertainty evaluation remains an issue yet to be resolved. In this regard, one topic that has received attention is the determination of the geometric errors of the CT instrument, which cause errors to dimensional measurements. Using an imaging method to quantify the geometric errors, process behavior charts are detailed and used to check the consistency of the CT measurement process with regard to the geometric errors. The utilization of process behavior charts is the major contribution of this work since they allow us to operate the measurement process up to its full potential, and to evaluate the actual effectiveness of the imaging method to quantify the errors.

The development of sustainable manufacturing processes and the associated reduction of operating materials such as cooling lubricants represent key focuses on recent production technology. Thus, a novel tool system with a closed-loop internal cooling system for turning operations is presented in this study. The system design is based on numerical investigations of the heat flow Q⋅ with consideration of the thermomechanical load of the cutting tool and flow characteristics of the cooling fluid. The process behavior of the proposed system shows a significant dependence of the flow rate V⋅ and tool thickness tT in terms of tool temperature reduction ΔθT.

Ransomware attacks are one of the biggest and attractive threats in cyber security today. Anti-virus software’s are often inefficient against zero-day malware and ransomware attacks, important network infections could result in a large amount of data loss. Such attacks are also becoming more dynamic and able to change their signatures – hence creating an arms race situation. This study investigates the relationship between a process behavior and its nature, in order to determine whether it is ransomware or not. The paper aim is to see if using this method will help the evading malicious software’s and use as a self-defense mechanism using machine learning that emulates the human immune system. The analysis was conducted on 7 ransomware, 41 benign software, and 34 malware samples. The results show that we are able to distinguish between ransomware and benign applications, with a low false-positive and false-negative rate.

New drying strategies that use low temperatures can have a significant impact on the improvement of food quality, in particular regarding the retention of flavor compounds, bioactives and other thermosensitive components. The vacuum spray dryer (VSD) is a spray dryer that operates with a low-pressure drying chamber, which consequently reduces the increases the thermodynamic driving force for water removal and allows drying at significantly reduced temperatures. In order to understand the process behavior and define operational strategies, a mathematical model that encompasses mass and energy balances was validated with experimental measurements of pressure and temperature during drying of large chained maltodextrin (dextrose equivalent = 10). Results from experiments carried out in a pilot VSD present a good fit with the proposed model and confirmed its underlying assumptions. In addition, comparative analyses were performed regarding physical aspects of particles produced by VSD and by conventional spray dryer (SD) in the same equipment, but without vacuum. Under the tested conditions, VSD particles presented a higher moisture content (8%) and smaller time of wettability than SD particles. The morphological changes were caused by the vacuum and can be interesting for technological applications.

Mining local patterns of process behavior is a vital tool for the analysis of event data that originates from flexible processes, which in general cannot be described by a single process model without overgeneralizing the allowed behavior. Several techniques for mining local patterns have been developed over the years, including Local Process Model (LPM) mining, episode mining, and the mining of frequent subtraces. These pattern mining techniques can be considered to be orthogonal, i.e., they provide different types of insights on the behavior observed in an event log. In this work, we demonstrate that the joint application of LPM mining and other patter mining techniques provides benefits over applying only one of them. First, we show how the output of a subtrace mining approach can be used to mine LPMs more efficiently. Secondly, we show how instances of LPMs can be correlated together to obtain larger LPMs, thus providing a more comprehensive overview of the overall process. We demonstrate both effects on a collection of real-life event logs.

Selective laser sintering (SLS) of polymers is an additive manufacturing process that enables the production of functional technical components. Unfortunately, the SLS process is restricted regarding the materials that can be processed, and thus the resulting component properties are limited. The investigation in this study illustrates a new additive manufacturing process, which combines reactive liquids such as thermoset resins and thermoplastics to generate multi-material SLS parts. To introduce thermoset resins into the SLS process, the time-temperature-dependent curing behavior of the thermoset and the infiltration have to be understood to assess the process behavior. The curing properties were analyzed using a rotational viscometer. Furthermore, the fundamental infiltration behavior was analyzed with micro-dosing infiltration experiments. Additionally, the infiltration behavior was calculated successfully by using the Washburn equation. Finally, a thermoset resin in combination with a dosing system was chosen for integration in a laser sintering system.

Today, objective preconditions have been formed to find the ways on how to increase cost-effectiveness of NPPs operation, while providing the required safety level. One of such ways to increase thermal nominal power of power unit. The paper provides for the results of reactor behavior analysis at increased thermal power above nominal received using a one-dimensional system computer code RELAP5/MOD3.2 and relevant model of VVER-1000 (V-320) power unit. Calculation analyses are performed for quasi-static reactor operating conditions and transients using realistic approach in terms of initial performance parameters of reactor installation. In researches, representative initial events for transients have been selected according to the principle described further. For an abnormal operation, an event has been selected based on its high frequency and consequences, which require decreasing reactor power down to 50 % of nominal thermal power. For emergency conditions an event has been selected which is caused by external extreme impacts typical for Ukrainian NPP sites resulting in the worst consequences. Thus, the transients are represented by events associated with failure of a single turbine-driven feed water pump and total station blackout unit. To analyze emergency conditions caused by long-term blackout, they were additionally accompanied by a leakage through reactor coolant pump seals. Given that increase of steam flow in a turbine at increased thermal power above nominal requires additional studies on residual service life assessment of its critical components, a 3-D model of high-pressure rotor of a full speed turbine is proposed for further studies. Based on the calculations a comparative analysis of major parameters of the reactor at rated and increased thermal power is performed with assessment of significant factors to be considered in further studies on increase of installed thermal output of NPP unit.

Predicting process behavior in terms of the next activity to be executed and/or its timestamp can be crucial, e.g., to avoid impeding compliance violations or performance problems. Basically, two prediction techniques are conceivable, i.e., global and local techniques. Global techniques consider all process behavior at once, but might suffer from noise. Local techniques consider a certain subset of the behavior, but might loose the “big picture”. A combination of both techniques is promising to balance out each others drawbacks, but exists so far only in an implicit and unsystematic way. We propose LoGo as a systematic combined approach based on a novel global technique and an extended local one. LoGo is evaluated based on real life execution logs from multiple domains, outperforming nine comparison approaches. Overall, LoGo results in explainable prediction models and high prediction quality.

The growing interest in improving software processes has led organizations to aim for high maturity, where statistical process control (SPC) is required. SPC makes it possible to analyze process behavior, predict process performance in future projects and monitor process performance against established goals. The selection of measures for SPC can be a challenging task. Although the literature suggests measures for SPC, information is fragmented. With an aim towards providing a consolidated set of measures for SPC, as well as processes and goals related to these measures, we investigated the literature through a systematic mapping. Following that, we applied a questionnaire to three professionals from Brazilian organizations to check whether the measures they have used in SPC initiatives could also be found in literature. In this paper we discuss our main findings related to the 47 goals, 15 processes and 84 measures identified considering the systematic mapping and the questionnaire results.

Information systems from various domains record their behavior in a form of event logs. These event logs can be further analyzed and formal process models describing hidden processes can be discovered. In order to relate real and expected process behavior, discovered (constructed from event logs) and reference (manually created by analysts) process models can be compared. The result of comparison should clearly present commonalities and differences between these models. Since most process models are represented by graph-based languages, a graph comparison technique can be applied. It is worth known that graph comparison techniques are computationally expensive. In this paper, we adapt different heuristic graph comparison algorithms to compare BPMN (Business Process Model and Notation) models. These algorithms are implemented and tested on large BPMN models discovered from event logs. We show that some of the heuristic algorithms allow to find nearly optimal solutions in a reasonable amount of time.

Reducing costs and increasing equipment availability (uptime) are among the main goals of industrial ventures. Well defined interval durations between maintenance inspections provide major support in achieving these targets. However, in order to establish the best interval length, process behavior, cycle times and related costs must be clearly known, and future estimates for these parameters must be established. This paper applies process mining techniques in developing a probabilistic model in Bayesian Networks integrated to predictive models. The probability of a given activity occurring in the probabilistic model output establishes the forecast boundaries for predictive models, responsible for estimating process cycle times. Availability (uptime) and cost functions are mathematically defined and an iterative process is performed in the length of intervals between maintenance inspections until the time and costs wasted are minimized and the best interval duration is found. The probabilistic model enables simulating changes in the event occurrence probability, allowing a number of different scenarios to be visualized and providing better support to managers in scheduling maintenance activities. The results show that production losses can be further reduced through optimally defined intervals between maintenance inspections.

In micro forming, a precise planning of processes constitutes a challenging task due to inherent uncertainties. The method “Micro - Process Planning and Analysis” uses cause-effect networks to achieve this task. This article presents a method, which enables updating these networks based on new data regularly. It enables the use of discrete and continuous machine data, such as force profiles. Finally, the method is evaluated using simulated data, modelled according to a real process. The results show a quick registration of changes in the process behavior and a high prediction accuracy in terms of the expected mean and variance.

Fault prediction has arisen as a basic monitoring strategy that predicts an abnormal event occurring in near future based on the current symptoms observed in a process. Such a proactive approach helps in taking an appropriate action beforehand so as to mitigate the impact a fault can have on a process. Recently, data-driven modeling techniques have been widely used due to an increased accessibility to process data. Though the offline trained models are successful in modeling complex dynamics, they have limited ability in capturing the dynamic process behavior, especially under abnormal conditions. To address this issue, we utilize an adaptive modeling technique called operable adaptive sparse identification of systems (OASIS) that can cope with any dynamical changes. Based on the forecasted process behavior using OASIS, we perform risk-assessment to predict faults and assess risk. In the proposed method, risk is used as a criteria to monitor and manage process operation.

The internal threading with tap or forming tap, is a widely applied factoring process throughout industry. However, the knowledge held by the toolmakers is not transferred to the scientific literature, which justifies the small number of studies regarding this matter. This data limitation reduces the tool and cutting condition selection guide to geometrical characteristics. This current work has two main goals; the first one is to study the process behavior regarding the secondary and uncontrollable characteristics, such as thread length, tool coating, feed rate, and hole’s diameter. It also investigated the load distribution in the cutting edge for a tap operation. The second goal of this work is the investigation of the behavior of a new type of floating system in terms of torque and axial force. One of the outcomes of this work was that the torque distribution along the fillet was not uniform, with the torque of the third fillet higher than the others. The floating system tested reduces the axial force and torque by reducing synchronicity errors even in extremely conditions.

El artículo aborda tanto el problema ¿cuáles han sido los criterios utilizados por la Corte IDH para valorar la razonabilidad del plazo en los procesos penales ordinarios adelantados en Colombia como Estado Parte de la Convención Americana?, como el objetivo general de identificar los nichos citacionales que la Corte Interamericana de Derechos Humanos (CIDH) ha establecido en relación con la pregunta objeto de estudio. El resultado se consigue mediante el desarrollo de una investigación básica, descriptiva, deductiva y mediante la creación de una línea jurisprudencial, obteniendo que el plazo razonable es una institución jurídica que ha sido desarrolla en sus aspectos adjetivos y sustantivos. Estos últimos buscan garantizar una tutela judicial efectiva, y que deben ser valorados bajo cuatro criterios, pero no, de forma generalizada sino caso por caso: i) la complejidad del asunto; ii) la actividad procesal del interesado; iii) la conducta de las autoridades judiciales y; iv) la afectación generada en la situación jurídica de la persona involucrada en el proceso. Por último, que el plazo razonable en un proceso debe ser contabilizado desde la primera actuación procesal hasta la etapa de la ejecución y cumplimiento de la sentencia

An approach for integrated transient simulation of a BFB boiler furnace and the coupled steam cycle is presented. The BFB furnace is simulated with in-house CFD models implemented in the ANSYS Fluent® software. The boiler steam cycle with control loops is simulated with the dynamic process simulation software Apros®. The paper describes the steps taken to couple the modeling tools and discusses the way the numerical solutions can proceed in unison. The approach is demonstrated for a commercial BFB boiler. A fast load change situation was selected as the test case. A coupled transient simulation was carried out and the process behavior during the transient was analyzed. Critical properties affecting the feasibility of a given ramp rate are the emission release rates and temperature transients in the boiler. These properties are monitored during the transient simulation. The paper presents these results along with instantaneous data that show the spatial distribution of temperature and gas composition in the furnace. The paper shows that the modeling approach is feasible. Applicability of the approach and possible future improvements are discussed.

The injection-molding industry has recognized that computer-aided engineering (CAE) enhances an engineer?s ability to handle all aspects of the polymer injection-molding process, benefiting productivity, product quality, timeliness, and cost. The process behavior predicted by CAE can help novice engineers overcome the lack of previous experience and assist experienced engineers in that may otherwise be overlooked. By using CAE analysis to iterate and evaluate alternative designs and competing materials, engineering know-how in the form of design guidelines can be established relatively faster and more cost-effectively. This is illustrated by a wealth of literature and the ever-growing number of CAE software users in the injection-molding industry which has been highlighted in the article. Also the CAE software C-MOLD and the modeling of the thermoplastic injection moulding with the help of the software discussed briefly.

Process mining makes it possible to solve a task of finding and analyzing deviations in the process. System event logs record information about real process behavior. Weaknesses and errors of a workflow can be found during the analysis of logs. This is especially important in areas associated with significant responsibility and risk.

The main purpose of our research is to propose original algorithm to evaluate the dynamic behavior of processes from the survey data collected with the help of periodically repeated surveys based on Likert scale questions. This approach supposes the usage of AHP (Analytic Hierarchy Process) for assessment the factors influencing the process behavior. Our idea is to use the aggregated periodic rating scale data as alternatives inputs for AHP evaluation. The practical usefulness of proposed process quality evaluation technique was proved by examining particular Polish rehabilitation hospital service quality changes over time frame from 2008 to 2017.

Journal of manufacturing and materials processing 5(1), 4 (2021). doi:10.3390/jmmp5010004 special issue: "Special Issue "Progress in Precision Machining" / Special Issue Editors: Prof. Dr. Bernhard Karpuschewski, Guest Editor; Dr. Oltmann Riemer, Guest Editor", Published by MDPI, Basel

This study explains the yarn pull-out process behavior of woven fabrics in relation to their mechanical properties. Empirical research on the relationship between the yarn pull-out behavior and fabric properties are evaluated, along with a detailed comparison of friction between the fabric fibers in static and dynamic modes. Samples are tested in three modes, namely, neat, dissolved liquid, and silica particle- based Shear thickening fluid (STF)-treated fabric. Accordingly, the presence of STF increases friction between the fabrics and the warp and weft yarns, as well as prevents the displacement of the yarns. Increased friction also leads to an increase in the energy absorption of the yarn pull-out process. In this research, the pull-out test has been performed at three different velocities: 50, 250 and 500 mm/min. Results show that increases in the pull-out velocity increase the pull-out force of the neat and dissolved liquid samples. By contrast, the behavior is completely opposite in the STF-treated sample. Comparing the yarn pull-out values indicates that the STF-treated samples have the highest value, which is approximately three times higher than that of the neat sample.

This paper presents the results of simulation and experiments conducted around the application of controlled kinematics and chip removal conditions during infeed grinding cycles. The method consists on the application of continuous variation strategies of the three main speed variables that govern the removal mechanism and therefore the tribo-thermo-mechanical behavior of the process: grinding wheel speed, workpiece speed and feed rate. With the help of a time-domain simulation, the influence of this method on the process behavior is analyzed demonstrating numerous potential applications of the variable cycles by comparing with conventional ones. Tests have been performed to validate this technology and demonstrate the improvements of the variable cycles.

New leaching agents could lead to a reduction in ammonia nitrogen pollution and the supplementation of soil nutrients during in-situ mining. They could also result in the release of even more toxic heavy metals, which has an impact on the environment as well as human health. In this study, column leaching experiments were used to simulate in-situ leaching, and the leaching behavior and fractional changes of lead in weathered crust elution-deposited rare earth ore by different leaching agents were studied. The experimental results showed that the amount of lead that was leached followed the order of CLA (60% CaCl2 + 25% NH4Cl + 15% MgSO4) > (NH4)2SO4 > MgSO4. The lead leaching process was comprised of both an acceleration and deceleration stage that followed the first order kinetic model. The amount of Pb when using compound agent was the greatest most likely because of the presence of Cl−. The soil heavy metal morphology test showed that the three leaching agents primarily leached acid extractable lead, and the compound leaching agent leached the greatest amount of acid extractable Pb, which mainly due to the presence of NH4Cl. The reducible fraction was enriched in the direction of migration of the leachate, which was due to the presence of SO42−. These results indicate that the introduction of leaching agents during the mining process pose a greater risk for the release of heavy metals and provide a theoretical basis for the prevention and remediation of heavy metal pollution in mining areas where new leaching agents were used.

The present paper numerically and experimentally investigates the stall inception mechanisms in a centrifugal compressor with volute. Current studies about stall inception pay more attention on the axial compressors than the centrifugal compressors; especially, the circumferential position of stall inception onset and the stall process in the centrifugal compressor with asymmetric volute structure have not been studied sufficiently yet. In this work, the compressor performance experiment was conducted and the casing wall static pressure distributions were obtained by seventy-two static pressure sensors firstly. Then, the full annular unsteady simulations were carried out at different stable operating points, and the time-averaged static pressure distributions were compared with the experimental results. Finally, the stall process of the compressor was investigated by unsteady simulations in detail. Results show that the stall inception onset is determined by the impeller leading edge spillage flow, and the occurrence time of trailing edge backflow is prior to the leading edge spillage. The non-uniform static pressure circumferential distribution at impeller outlet induced by volute tongue causes the two stall inception regions locating at certain circumferential positions, which are 120° and 300° circumferential positions at impeller leading edge, corresponding to the circumferential static pressure peak and bulge regions at impeller outlet, respectively. In detail, at rotor revolution 2.86, a small disturbance that the incoming/tip clearance flow interface is perpendicular to axial direction occurs at 120° position, but this disturbance did not cause the compressor stall. Then at revolution 7, the first stall inception zone (spillage region) occurs at 120° position, causing the compressor stall with positive pressure ratio performance. At approximately revolution 23, the second stall inception zone occurs at about 300° position; however, both the intensity and size of this stall inception zone are smaller than those of the first stall inception zone. These two stall inception zones are not moving along circumferential direction because the stall inception circumferential position is dominated by the impeller outlet static pressure distribution. Even that, the obvious low frequency signals appear after the spillage crossing two blade leading edges; because at this moment, the spillage vortex caused by the tip leakage flow begins to shed. However, due to the asymmetric structure limitation, this vortex cannot move across full annular. Furthermore, the spillage vortexes cause the local low static pressure zone ahead of blade leading edge in the centrifugal compressor with volute, suggesting that the spillage can be predicted by the steady casing wall static pressure measuring. The development of blockage zones at impeller leading edge is also investigated quantitatively by analyzing the stall blockage effect.

The paper proposes the use of Linear Temporal Logic (LTL) formulas for the behavioral description of the traces corresponding to log files. Such descriptions are used to group similar traces into classes applying standard clustering techniques. The classification results are used to feed a machine learning system able to predict, after a few initial events, the cluster to which an in-execution process is probably going to belong. The prediction model could be used to feed an on-line recommendation system so as to drive the process towards a desired cluster or to prevent it from being part of a non-desired one. The paper describes the used methodology and shows its validity by means of the application to a real log.

International audience; This paper presents a method for the monitoring and prediction of the electrolyte quality during the process of copper electroplating. This is important in industry, as any deviation in the solution quality leads to a deterioration of the quality of the processed products. The aim of the study is to identify some physical parameters that are representative of the quality variation during the deposition process. These parameters are then tracked online to continuously assess the solution quality and predict its remaining useful life. To do this, the process behavior is first characterized to derive a nominal model and to identify the physical parameters that can be used to describe the aging variation in the electrolyte quality. The aging model is then explored to assess the current level of the solution quality and to predict its remaining useful life. The proposed method is verified using real data acquired from a specifically designed test bench. The obtained results reveal the efficiency of the method.

The combination of nondeterminism and probability in concurrent systems lead to the development of several interpretations of process behavior. If we restrict our attention to linear properties only, we can identify three main approaches to trace and testing semantics: the trace distributions, the trace-by-trace and the extremal probabilities approaches. In this paper, we propose novel notions of behavioral metrics that are based on the three classic approaches above, and that can be used to measure the disparities in the linear behavior of processes wrt trace and testing semantics. We study the properties of these metrics, like non-expansiveness, and we compare their expressive powers., In Proceedings EXPRESS/SOS 2018, arXiv:1808.08071

We develop a multilevel model to explain how affect and transformational leadership (TFL) influence team creative process behavior. We theorize, in particular, that a dual-level model of TFL, which incorporates both individual-focused TFL (addressing the individual differences of team members) and group-focused TFL (addressing the team as a whole) underlies affect–creativity relationships. We argue moreover that these effects exist across three conceptual levels: (1) within-person, (2) interpersonal, and (3) group. We conclude with a discussion of the theoretical implications and limitations of our model and suggest potential avenues for future research.

In this study, an alternative sugar beet processing method using alkaline pressing of electroporated cossettes was investigated. The cossettes were first treated by pulsed electric fields at ambient temperature (T = 12 °C) with a strength of E = 600 V/cm for t PEF = 10 ms. The electroporated cossettes were then subjected to a double pressing with intermediate lime impregnation. The lime quantity used was varied from 0 to 1 % (g CaO/100 g fresh cossettes). Liming of electroporated cossettes enhances the second pressing kinetics and improves the quality of raw juice. With lime addition in quantity of 0.6 % (g CaO/100 g fresh cossettes), the juice yield was increased by 12 %, the juice purity was higher (93.61 vs 93.07 %), the juice was less colored (2938 vs 4875 IU), and contained less colloids (1.96 vs 2.75 g/L) and proteins (197 vs 261 mg/L) comparing to the juice obtained from electroporated but nonlimed cossettes. The dryness of pulp was increased from 25.72 % without liming to 38.5 % using the optimal lime quantity. Results also suggest that a first pressing of 20 % was sufficient to impregnate the optimal lime quantity before the second pressing. The maximal juice yield estimated from the empirical compression model attained about 90 % under 15 bar.

To implement optimization and control on gold cyanidation leaching process (GCLP), it is an important prerequisite to establish an accurate process model. In this paper, a hybrid model in serial structure was proposed, where a first-principle model based on mass conservation equations was presented to describe the basic process behavior and its unknown kinetic reaction rates were predicted using BP ANN models without any structures considered. The proposed serial hybrid model had been applied to the prediction of gold recovery of the GCLP in a gold treatment plant. The results indicate that the proposed serial hybrid model has better prediction performance and generalization ability than the pure mechanistic model. To further reduce the effect of prediction error (plant-model mismatch) on real time optimization (RTO), modifier adaptation approach had been investigated and implemented to the GCLP. The result shows that when model mismatches with the actual plant or larger process disturbance occurs, significant reduction of production cost can be actualized iteratively by implementing the proposed adaptive RTO strategy.

This paper has demonstrated the valorization of inexpensive and readily available restaurant waste containing most consumed food and beverage residues as adsorbents for methylene blue dye. Coffee, tea, lettuce and citrus waste have been utilized without any pre-treatment, thus the adsorption capacities and dye removal efficiency were determined. Coffee waste showed the highest adsorbent capacity, followed by tea, lettuce and citrus waste. The dye removal was more effective as dye concentration increases from 5 up to 60 mg/L. The favorable results obtained for lettuce waste have been especially encouraged, as this material has not been commonly employed for sorption purposes. Equilibrium data fitted very well in a Freundlich isotherm model, whereas pseudo-second- order kinetic model describes the process behavior. Restaurant waste performed rapid dye removal at no cost, so it can be adopted and widely used in industries for contaminated water treatment. Metilen plavo je bazna boja sa strukturom heterocikličnog aromatičnog jedinjenja, koja se u dobu savremenog društva često koristi za bojenje raznih tipova materijala. Efluenti iz industrije boja predstavljaju jednu od najproblematičnijih vrsta otpadnih voda za tretiranje, usled njihove velike biološke i hemijske potrebe za kiseonikom. Pored toga, toksičnost i teško razgradiva priroda boja mogu značajno uticati na fotosintetičku aktivnost vodenog sveta. Adsorpcijom na čvrstim nosačima mogu se ukloniti, odnosno, svesti na minimum razni tipovi zagađivača, zbog čega ova tehnika ima široku primenu u kontroli zagađenja voda. S tim u vezi, ispitivana je mogućnost iskorišćenja sirovog otpada iz restorana za adsorpciju i uklanjanje boje metilen plavo iz vodenih rastvora. U radu su određivani adsorpcioni kapaciteti otpadnih sirovina najčešće konzumiranih namirnica, uključujući kafu, čaj, zelenu salatu i mešavinu citrusnog voća (citrusa). Proces adsorpcije izveden je u šaržnom režimu, uz mešanje na tresilici, u uslovima ambijentalne temperature. Efikasnost procesa praćena je u zavisnosti od promene koncentracije boje u radnim rastvorima. Pokazano je da otpadna kafa ispoljava najviši adsorpcioni kapacitet, a za njom otpadni čaj, zelena salata i citrusi, redom. Nakon 30 min odigravanja reakcije, na kafi se vezalo gotovo 90%, dok na čaju 80%, zelenoj salati 60% i citrusima 30% od početne koncentracije rastvora boje. Vezivanje boje na adsorbentima bilo je efikasnije u slučajevima kada je koncentracija rastvora boje rasla od 5 do 60 mg/L. Na osnovu ispitivanja najčešće primenjivanih modela izo- termi za definisanje adsorpcione ravnoteže, pronađeno je da su ravnotežni podaci, u slučajevima svih vrsta adsorbenata, bili u skladu sa Frojndlihovom adsorpcionom izotermom. S tim u vezi, smatra se da je proces odigravanja reakcije baziran na interakcijama između molekula boje i funkcionalnih grupa na heterogenoj površini adsorbenata. Kinetika i mehanizam reakcija opisani su po principu modela pseudo-drugog reda, koji se zasniva na pretpostavci da hemisorpcija, odnosno stvaranje hemijskih veza između adsorbenta i adsorbata, kontroliše celokupan tok reakcija. Iskorišćenjem sirovog otpada iz restorana može se doći do ekonomski isplativog rešenja za brzo i efikasno uklanjanje toksičnih i teško raz- gradivih boja iz vodenih rastvora. Smatra se da ova vrsta otpadnog materijala može naći svoju primenu u brojnim adsorpcionim procesima, u prvom redu onim koji se zasnivaju na tretmanima prečišćavanja otpadnih i pijaćih voda.

Neural networks are a tool that can be used for the modelling of many systems and process behavior. The artificial neural networks can “understand” the information from health care processes. For the estimations between these two concepts we use intuitionistic fuzzy sets. Here, for the learning process of the neural networks, we will use 60 heavy oils that have been characterized for their distillation characteristics by ASTM D-5236 and ASTM D-1160 in the Research laboratory of LUKOIL Neftochim Burgas. The aim is to recognize the type of crude oil based on six of their properties.

The aim of technical processes supervision is to reveal the actual system state and also undesired states to be indicated. The deviations from normal process behavior are usually results of faults. They might cause malfunctions or failures that must be prevented. The monitoring of a technical process in normal operation state is a part of the supervision process. This is usually performed by a limit-checking of some measurable output variables. Certain alarms rise if the variables values are found outside of the tolerance zone set.

Conventional and ultrasound assisted extraction of anthocyanins from grape pomace are here analyzed and compared. Mathematical modeling is used firstly to represent the extraction process and determine the associated mass transport parameters, and afterwards, to obtain useful predictions on how the system behaves under different operating conditions. The mathematical model here developed is based on first-principles, in order to more accurately describe the underlying phenomena that govern the extraction process behavior. Extraction of anthocyanins from grape pomace is performed using a hydro alcoholic solution as solvent, and experimental runs at different temperatures were carried out for both conventional and ultrasound-assisted techniques. A good agreement between experimental and computed extraction yields was achieved as the reported statistical parameters indicate. Obtained results highlight the performance differences between both processes, and pinpoint which variables impact the most in the extraction yield. Fil: Bonfigli, M.. Universidad Tecnológica Nacional. Regional Rosario. Centro de Aplicaciones Informáticas y Modelado en Ingeniería; Argentina Fil: Godoy, E.. Universidad Tecnológica Nacional. Regional Rosario. Centro de Aplicaciones Informáticas y Modelado en Ingeniería; Argentina Fil: Reinheimer, Maria Agustina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; Argentina. Universidad Tecnológica Nacional. Regional Rosario. Centro de Aplicaciones Informáticas y Modelado en Ingeniería; Argentina Fil: Scenna, Nicolas Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; Argentina. Universidad Tecnológica Nacional. Regional Rosario. Centro de Aplicaciones Informáticas y Modelado en Ingeniería; Argentina

This chapter provides a brief introduction to industrial processes, classifications, physical structure, application, and behavior. The chapter further explains the automation system along with its basic functions and its role in managing industrial processes to achieve desired results. The chapter ends with the needs met by automation and the benefits of automation.

Leadership behaviors of undergraduates (n=1103) were examined using the Student Leadership Practice Inventory (SLPI). The practice of leadership behaviors increased significantly from freshman to juniors and from juniors to seniors. However, each class was significantly less likely to practice the Challenge the Process behavior and significantly more likely to practice the Enable Others to Act behavior. Experience with different types of leadership roles were related to higher SLPI practice scores. Age, race, transfer status and taking a freshman experience seminar did not influence SLPI scores or the number of different types of leadership roles. Unexpectedly, women had significantly higher SLPI scores than men at each class level. DOI: 10.12806/V13/I1/R4

Die Vergroserung des effizienten Anwendungsbereiches von Sortierprozessen in den Bereich feinster Partikelsysteme (0,1 – 10 μm) stellt eine erhebliche Herausforderung fur die Forschung dar. Ein moglicher Prozessansatz zur Verbesserung des Sortierergebnisses ist die Flussig/Flussig-Flotation. In dieser Studie werden Aussagen bezuglich Anwendbarkeit und Prozessverhalten dieses Ansatzes in Abhangigkeit von ausgewahlten Prozessparametern an verschiedenen Modellpartikelsystemen prasentiert. Increasing the range of application of separation processes to effectively separate ultrafine particles (0.1 – 10 μm) represents a significant research challenge. A possible process approach to enhance the separation result is two-liquid flotation. In this investigation, conclusions regarding the applicability and the process behavior of this approach are presented for different model particle systems with respect to selected process parameters.

A new route for the synthesis of WC–Al 2 O 3 composite free from unwanted W 2 C phase through mechano-chemical treatment of WO 3 – x Al– y (4 − 1.5 x )C mixture has been investigated. It has been concluded that by substitution of 10 mol% of 2 mol Al ( x = 1.8) with equivalent stoichiometric amount of C together with 20 mol% excess carbon addition ( y = 1.2), i.e., using a WO 3 :1.8Al:1.56C initial mixture, W 2 C free WC–Al 2 O 3 composite can be successfully obtained by about 6 h milling. Crystallite size and lattice strain of the WC–Al 2 O 3 composite synthesized in the present study as well as those obtained by the previous successful routes have been reported. The results revealed that a powder whose synthesis route involves lower adiabatic temperature as well as longer milling time would have a smaller crystallite size and higher lattice strain. Furthermore, TEM observations showed that all synthesized powders contain the agglomerates which consist of nano-size particles.

Capturing the performance of a system or business process as accurately as possible is important, as models enriched with performance information provide valuable input for analysis, operational support, and prediction. Due to their computationally nice properties, memoryless models such as exponentially distributed stochastic Petri nets have earned much attention in research and industry. However, there are cases when the memoryless property is clearly not able to capture process behavior, e.g., when dealing with fixed time-outs. We want to allow models to have generally distributed durations to be able to capture the behavior of the environment and resources as accurately as possible. For these more expressive process models, the execution policy has to be specified in more detail. In this paper, we present and evaluate process discovery algorithms for each of the execution policies. The introduced approach uses raw event execution data to discover various classes of stochastic Petri nets. The algorithms are based on the notion of alignments and have been implemented as a plug-in in the process mining framework ProM. Keywords: Process mining; Stochastic Petri nets; Generally distributed transitions

O etanol é um importante biocombustível alternativo para reduzir a dependência dos combustíveis fósseis. A produção de etanol por via fermentativa é a mais difundida no Brasil. Avanços tecnológicos na fermentação podem elevar a competitividade do etanol e torná-lo ainda mais atrativo. Uma ferramenta que auxilia no avanço do processo fermentativo é o desenvolvimento e aplicação de modelos matemáticos. Eles podem ser utilizados para prever e avaliar o comportamento do processo em diferentes condições operacionais. Assim, o objetivo deste trabalho é realizar uma revisão para apresentar e analisar os modelos cinéticos da literatura acerca da modelagem matemática da produção de etanol. Há diversos modelos empíricos na literatura para representar a cinética fermentativa, cada um com suas características específicas. Há modelos que apresentam inibição por produto, substrato e células. A decisão de qual modelo utilizar é inteiramente dependente do meio em que a fermentação ocorre.ABSTRACTEthanol is an important alternative biofuel to reduce dependence of fossil fuels. The production of ethanol by fermentation is the most widespread in Brazil. Technological advances in fermentation can increase the competitiveness of ethanol and make it even more attractive. A tool that helps in the advancement of the fermentation process is the development and application of mathematical models. They can be used to predict and evaluate the process behavior under different operating conditions. The objective of this paper is to do a review to present and analyze the kinetic models of the literature on mathematical modeling of ethanol production. There are several empirical models in the literature to represent the fermentation kinetics, each one with their specific characteristics. There are models that show inhibition by product, substrate and cells. The decision of which model to use is entirely dependent on the environment in which fermentation occurs.