Aplica����o e avalia����o de desempenho de um sistema de otimiza����o em tempo real em uma unidade de produ����o de propeno.

Com o objetivo de aumentar o lucro de plantas qu��micas, a Otimiza����o em Tempo Real (RTO) �� uma ferramenta que busca determinar as condi����es ��timas operacionais do processo em estado estacion��rio, respeitando as restri����es operacionais estabelecidas. Neste trabalho foi realizada a implementa����o pr��tica de um ciclo RTO em um processo de destila����o por recompress��o de vapor (VRD), propileno-propano, da Refinaria de Paul��nia (Petrobras S.A.), a partir de dados hist��ricos da planta. Foram consideradas as principais etapas de um ciclo cl��ssico de RTO: identifica����o de estado estacion��rio, reconcilia����o de dados, estima����o de par��metros e otimiza����o econ��mica. Essa unidade foi modelada, simulada e otimizada em EMSO (Environment for Modeling, Simulation and Optimization), um simulador de processos orientado a equa����es desenvolvido no Brasil. Foram analisados e comparados dois m��todos de identifica����o de estado estacion��rio, um baseado no teste estat��stico F e outro baseado em wavelets. Ambos os m��todos tiveram resultados semelhantes e mostraram-se capazes de identificar os estados estacion��rios de forma satisfat��ria, embora seja necess��rio o ajuste de par��metros na sua implementa����o. Foram identificados alguns pontos estacion��rios para serem submetidos ao ciclo RTO e foi poss��vel verificar a import��ncia de partir de um estado estacion��rio para a continuidade do ciclo, j�� que essa �� uma premissa do m��todo. A partir dos pontos analisados, os resultados deste estudo mostram que o RTO �� capaz de aumentar o ganho econ��mico entre 2,5-24%, dependendo das condi����es iniciais consideradas, o que pode representar ganhos de at�� 18 milh��es de d��lares por ano. Al��m disso, para essa unidade, verificou-se que o compressor �� um equipamento limitante no aumento de ganho econ��mico do processo.

Speaker Normalization Using Cortical Strip Maps: A Neural Model for Steady State Vowel Identification

Auditory signals of speech are speaker-dependent, but representations of language meaning are speaker-independent. Such a transformation enables speech to be understood from different speakers. A neural model is presented that performs speaker normalization to generate a pitchindependent representation of speech sounds, while also preserving information about speaker identity. This speaker-invariant representation is categorized into unitized speech items, which input to sequential working memories whose distributed patterns can be categorized, or chunked, into syllable and word representations. The proposed model fits into an emerging model of auditory streaming and speech categorization. The auditory streaming and speaker normalization parts of the model both use multiple strip representations and asymmetric competitive circuits, thereby suggesting that these two circuits arose from similar neural designs. The normalized speech items are rapidly categorized and stably remembered by Adaptive Resonance Theory circuits. Simulations use synthesized steady-state vowels from the Peterson and Barney [J. Acoust. Soc. Am. 24, 175-184 (1952)] vowel database and achieve accuracy rates similar to those achieved by human listeners. These results are compared to behavioral data and other speaker normalization models.

The validity of critical speed determined from track cycling for identification of the maximal lactate steady state

The objective of this study was to determine the critical speed (CS) for track cycling and to assess whether a lactate steady state occurs at this speed. Fourteen competitive cyclists performed the following tests on an official cycling track (333.3 m): 1) incremental test for determination of the intensity corresponding to 4 mM of blood lactate (onset of blood lactate accumulation, OBLA) and maximal oxygen uptake (VO(2)max); 2) CS: 3 maximal bouts for distances of 2, 4 and 6 km executed in random order and with a period of recovery of 40 to 50 min between bouts. CS was determined for each subject from the linear regression between the distance and the time taking to cycle it; 3) Endurance test in which subjects were instructed to pedal at 100% of their individually determined CS for 30 min. At the 10(th) and 30(th) min (or upon exhaustion), 25 mul of blood were collected from ear lobe for later analysis of blood lactate [Lac]b. An increase less than or equal to1 mM between 10 and 30 min of exercise was considered as the criterion for the occurrence of the lactate steady state. CS (49.6 +/- 8.6 ml.kg(-1).min(-1); 36.9 +/- 2.7 km.h(-1)) was significantly higher than OBLA (43.7 8.0 ml.kg(-1).min(-1); 35.24 +/- 2.6 km.h(-1)) although the two parameters were highly correlated (r=0.97). During the endurance test, only 8 of the 14 subjects completed the 30 min period at CS. of these 8 subjects, only 2 presented a lactate steady state. Time to exhaustion at CS was 20.3 +/- 1.6 min for the remaining 6 subjects. The 12 subjects who did not reach a lactate steady state presented mean [Lac]b values of 7.4 +/- 1.3 mM at 10 min and of 9.4 +/- 1.9 mM at the end of the test (exhaustion), characterizing an exercise intensity of high lactacidemia. on the basis of the present results, we can conclude that CS determined by a track cycling test seems to overestimate the intensity of the maximal lactate steady state for most subjects.

Steady state optimization of an ammonia reactor

A hybrid simulation technique for identification and steady state optimization of a tubular reactor used in ammonia synthesis is presented. The parameter identification program finds the catalyst activity factor and certain heat transfer coefficients that minimize the sum of squares of deviation from simulated and actual temperature measurements obtained from an operating plant. The optimization program finds the values of three flows to the reactor to maximize the ammonia yield using the estimated parameter values. Powell's direct method of optimization is used in both cases. The results obtained here are compared with the plant data.

Steady-state modelling of the universal exhaust gas oxygen (UEGO) sensor

The universal exhaust gas oxygen (UEGO) sensor is a well-established device which was developed for the measurement of relative air fuel ratio in internal combustion engines. There is, however, little information available which allows for the prediction of the UEGO's behaviour when exposed to arbitrary gas mixtures, pressures and temperatures. Here we present a steady-state model for the sensor, based on a solution of the Stefan-Maxwell equation, and which includes a momentum balance. The response of the sensor is dominated by a diffusion barrier, which controls the rate of diffusion of gas species between the exhaust and a cavity. Determination of the diffusion barrier characteristics, especially the mean pore size, porosity and tortuosity, is essential for the purposes of modelling, and a measurement technique based on identification of the sensor pressure giving zero temperature sensitivity is shown to be a convenient method of achieving this. The model, suitably calibrated, is shown to make good predictions of sensor behaviour for large variations of pressure, temperature and gas composition. �� 2012 IOP Publishing Ltd.

Accelerated computation of cyclic steady state for simulated-moving-bed processes

Industrial applications of the simulated-moving-bed (SMB) chromatographic technology have brought an emergent demand to improve the SMB process operation for higher efficiency and better robustness. Improved process modelling and more-efficient model computation will pave a path to meet this demand. However, the SMB unit operation exhibits complex dynamics, leading to challenges in SMB process modelling and model computation. One of the significant problems is how to quickly obtain the steady state of an SMB process model, as process metrics at the steady state are critical for process design and real-time control. The conventional computation method, which solves the process model cycle by cycle and takes the solution only when a cyclic steady state is reached after a certain number of switching, is computationally expensive. Adopting the concept of quasi-envelope (QE), this work treats the SMB operation as a pseudo-oscillatory process because of its large number of continuous switching. Then, an innovative QE computation scheme is developed to quickly obtain the steady state solution of an SMB model for any arbitrary initial condition. The QE computation scheme allows larger steps to be taken for predicting the slow change of the starting state within each switching. Incorporating with the wavelet-based technique, this scheme is demonstrated to be effective and efficient for an SMB sugar separation process. Moreover, investigations are also carried out on when the computation scheme should be activated and how the convergence of the scheme is affected by a variable stepsize.

Stochastic chemical kinetics and the quasi-steady-state assumption : application to the stochastic simulation algorithm and chemical master equation

Recently the application of the quasi-steady-state approximation (QSSA) to the stochastic simulation algorithm (SSA) was suggested for the purpose of speeding up stochastic simulations of chemical systems that involve both relatively fast and slow chemical reactions [Rao and Arkin, J. Chem. Phys. 118, 4999 (2003)] and further work has led to the nested and slow-scale SSA. Improved numerical efficiency is obtained by respecting the vastly different time scales characterizing the system and then by advancing only the slow reactions exactly, based on a suitable approximation to the fast reactions. We considerably extend these works by applying the QSSA to numerical methods for the direct solution of the chemical master equation (CME) and, in particular, to the finite state projection algorithm [Munsky and Khammash, J. Chem. Phys. 124, 044104 (2006)], in conjunction with Krylov methods. In addition, we point out some important connections to the literature on the (deterministic) total QSSA (tQSSA) and place the stochastic analogue of the QSSA within the more general framework of aggregation of Markov processes. We demonstrate the new methods on four examples: Michaelis���Menten enzyme kinetics, double phosphorylation, the Goldbeter���Koshland switch, and the mitogen activated protein kinase cascade. Overall, we report dramatic improvements by applying the tQSSA to the CME solver.

Volatile properties of particles emitted by compressed natural gas and diesel buses during steady state and transient driving modes

Volatile properties of particle emissions from four compressed natural gas (CNG) and four diesel buses were investigated under steady state and transient driving modes on a chassis dynamometer. The exhaust was diluted utilising a full-flow continuous volume sampling system and passed through a thermodenuder at controlled temperature. Particle number concentration and size distribution were measured with a condensation particle counter and a scanning mobility particle sizer, respectively. We show that, while almost all the particles emitted by the CNG buses were in the nanoparticle size range, at least 85% and 98% were removed at 100��C and 250��C, respectively. Closer analysis of the volatility of particles emitted during transient cycles showed that volatilisation began at around 40��C with the majority occurring by 80��C. Particles produced during hard acceleration from rest exhibited lower volatility than that produced during other times of the cycle. Based on our results and the observation of ash deposits on the walls of the tailpipes, we suggest that these non-volatile particles were composed mostly of ash from lubricating oil. Heating the diesel bus emissions to 100��C removed ultrafine particle numbers by 69% to 82% when a nucleation mode was present and just 18% when it was not.

Analytical model for fault section estimation and state identification of unobserved protective relays in power systems [������������������������������������������������������������������������]

In recent years, some models have been proposed for the fault section estimation and state identification of unobserved protective relays (FSE-SIUPR) under the condition of incomplete state information of protective relays. In these models, the temporal alarm information from a faulted power system is not well explored although it is very helpful in compensating the incomplete state information of protective relays, quickly achieving definite fault diagnosis results and evaluating the operating status of protective relays and circuit breakers in complicated fault scenarios. In order to solve this problem, an integrated optimization mathematical model for the FSE-SIUPR, which takes full advantage of the temporal characteristics of alarm messages, is developed in the framework of the well-established temporal constraint network. With this model, the fault evolution procedure can be explained and some states of unobserved protective relays identified. The model is then solved by means of the Tabu search (TS) and finally verified by test results of fault scenarios in a practical power system.

How long does it take for aquifer recharge or aquifer discharge processes to reach steady state?

Groundwater flow models are usually characterized as being either transient flow models or steady state flow models. Given that steady state groundwater flow conditions arise as a long time asymptotic limit of a particular transient response, it is natural for us to seek a finite estimate of the amount of time required for a particular transient flow problem to effectively reach steady state. Here, we introduce the concept of mean action time (MAT) to address a fundamental question: How long does it take for a groundwater recharge process or discharge processes to effectively reach steady state? This concept relies on identifying a cumulative distribution function, $F(t;x)$, which varies from $F(0;x)=0$ to $F(t;x) \to \infty$ as $t\to \infty$, thereby providing us with a measurement of the progress of the system towards steady state. The MAT corresponds to the mean of the associated probability density function $f(t;x) = \dfrac{dF}{dt}$, and we demonstrate that this framework provides useful analytical insight by explicitly showing how the MAT depends on the parameters in the model and the geometry of the problem. Additional theoretical results relating to the variance of $f(t;x)$, known as the variance of action time (VAT), are also presented. To test our theoretical predictions we include measurements from a laboratory���scale experiment describing flow through a homogeneous porous medium. The laboratory data confirms that the theoretical MAT predictions are in good agreement with measurements from the physical model.

Volatile properties of CNG and Diesel bus emissions produced during steady state and transient driving modes

An analysis of the emissions from 14 CNG and 5 Diesel buses was conducted during April & May, 2006. Studies were conducted at both steady state and transient driving modes on a vehicle dynamometer utilising a CVS dilution system. This article will focus on the volatile properties of particles from 4 CNG and 4 Diesel vehicles from within this group with a priority given to the previously un-investigated CNG emissions produced at transient loads. Particle number concentration data was collected by three CPC���s (TSI 3022, 3010 & 3782WCPC) having D50 cut-offs set to 5nm, 10nm & 20nm respectively. Size distribution data was collected using a TSI 3080 SMPS with a 3025 CPC during the steady state driving modes. During transient cycles mono-disperse ���slices��� of between 5nm & 25nm were measured. The volatility of these particles was determined by placing a thermodenuder before the 3022 and the SMPS and measuring the reduction in particle number concentration as the temperature in the thermodenuder was increased. This was then normalised against the total particle count given by the 3010 CPC to provide high resolution information on the reduction in particle concentration with respect to temperature.

Nanoparticle emissions from a fleet of CNG buses in transient and steady state operation

Particle emission measurements from a fleet of 14 CNG and 5 Diesel buses were measured both for transient and steady state mode s on a chassis dynamometer with a CVS dilution system. Several transient DT80 cycles and 4 steady sate modes (0, 25, 50 100% of maximum load) were measured for each bus tested. Particle number concentration data was collected by three CPC���s (TSI 3022, 3010 3782WCPC) having D50 cut-offs set to 5, 10 and 20nm respectively. The size distributions were measured with a TSI 3080 SMPS with a 3025 CPC during the steady state modes. Particle mass emissions were measured with a TSI Dustrak. Particle mass emissions for Diesel buses were upto 2 orders of magnitude higher than for CNG buses. Particle number emissions during steady state modes for Diesel busses were 2 to 5 times higher than for CNG busses for all of the tested loads. On the other hand for the DT80 transient cycle particle number emissions were up to 3 times higher for the CNG buses. More detailed analysis of the transient cycles revealed that the reason for this was due to high particle number emissions from CNG busses during the acceleration parts of the cycles. Particles emitted by the CNG busses during acceleration were in the nucleation mode with the majority being smaller than 10nm. Volatility measurements have also shown that they were highly volatile.

Steady state and transient dynamometer vehicle emission measurements using a DiSC

Analysis of the particulate size and number concentration emissions from a fleet of inner city medium duty CNG buses was conducted using the newly available Diffusion Size Classifier in comparison with more traditional SMPS's and CPC's. Studies were conducted at both steady state and transient driving modes on a vehicle dynamometer utilising a CVS dilution system. Comparative analysis of the results showed that the DiSC provided equivalent information during steady state conditions and was able to provide additional information during transient conditions, namely, the modal diameter of the particle size distribution.