Robust integration of real time optimization with linear model predictive control

Here, we study the stable integration of real time optimization (RTO) with model predictive control (MPC) in a three layer structure. The intermediate layer is a quadratic programming whose objective is to compute reachable targets to the MPC layer that lie at the minimum distance to the optimum set points that are produced by the RTO layer. The lower layer is an infinite horizon MPC with guaranteed stability with additional constraints that force the feasibility and convergence of the target calculation layer. It is also considered the case in which there is polytopic uncertainty in the steady state model considered in the target calculation. The dynamic part of the MPC model is also considered unknown but it is assumed to be represented by one of the models of a discrete set of models. The efficiency of the methods presented here is illustrated with the simulation of a low order system. (C) 2010 Elsevier Ltd. All rights reserved.

Dodigen 213-N as corrosion inhibitor for ASTM 1010 mild steel in 10% HCL

This article describes a study of the behavior of a mixture of amines and amides, commercially known as Dodigen 213-N (D-213 N), as a corrosion inhibitor for ASTM 1010 mild steel in 10% w/w HCl solution. The concentration range used was 1 x 10(-5) M to 8 x 10(-4) M. The weight loss and electrochemical techniques used were corrosion potential measurement, anodic and cathodic polarization curves, and electrochemical impedance spectroscopy (EIS). The solution temperature was 50 +/- A 1 A degrees C and it was naturally aerated. The corrosion potential values shifted to slightly more positive values, thus indicating mixed inhibitor behavior. The anodic and cathodic polarization curves showed that D-213 N is an effective corrosion inhibitor, since both the anodic and the cathodic reactions were polarized in comparison with those obtained without inhibitor. For all concentrations the cathodic polarization curves were more polarized than the anodic ones. The inhibition efficiency was in the range 75-98%, calculated from values of weight loss and corrosion current density, i (corr), obtained by extrapolation of Tafel cathodic linear region.

On the Filtering Problem for Continuous-Time Markov Jump Linear Systems with no Observation of the Markov Chain

We consider in this paper the optimal stationary dynamic linear filtering problem for continuous-time linear systems subject to Markovian jumps in the parameters (LSMJP) and additive noise (Wiener process). It is assumed that only an output of the system is available and therefore the values of the jump parameter are not accessible. It is a well known fact that in this setting the optimal nonlinear filter is infinite dimensional, which makes the linear filtering a natural numerically, treatable choice. The goal is to design a dynamic linear filter such that the closed loop system is mean square stable and minimizes the stationary expected value of the mean square estimation error. It is shown that an explicit analytical solution to this optimal filtering problem is obtained from the stationary solution associated to a certain Riccati equation. It is also shown that the problem can be formulated using a linear matrix inequalities (LMI) approach, which can be extended to consider convex polytopic uncertainties on the parameters of the possible modes of operation of the system and on the transition rate matrix of the Markov process. As far as the authors are aware of this is the first time that this stationary filtering problem (exact and robust versions) for LSMJP with no knowledge of the Markov jump parameters is considered in the literature. Finally, we illustrate the results with an example.

Discrete-time mean variance optimal control of linear systems with Markovian jumps and multiplicative noise

In this article, we consider the stochastic optimal control problem of discrete-time linear systems subject to Markov jumps and multiplicative noise under three kinds of performance criterions related to the final value of the expectation and variance of the output. In the first problem it is desired to minimise the final variance of the output subject to a restriction on its final expectation, in the second one it is desired to maximise the final expectation of the output subject to a restriction on its final variance, and in the third one it is considered a performance criterion composed by a linear combination of the final variance and expectation of the output of the system. We present explicit sufficient conditions for the existence of an optimal control strategy for these problems, generalising previous results in the literature. We conclude this article presenting a numerical example of an asset liabilities management model for pension funds with regime switching.

Non-linear modal analysis for beams subjected to axial loads: Analytical and finite-element solutions

A rigorous derivation of non-linear equations governing the dynamics of an axially loaded beam is given with a clear focus to develop robust low-dimensional models. Two important loading scenarios were considered, where a structure is subjected to a uniformly distributed axial and a thrust force. These loads are to mimic the main forces acting on an offshore riser, for which an analytical methodology has been developed and applied. In particular, non-linear normal modes (NNMs) and non-linear multi-modes (NMMs) have been constructed by using the method of multiple scales. This is to effectively analyse the transversal vibration responses by monitoring the modal responses and mode interactions. The developed analytical models have been crosschecked against the results from FEM simulation. The FEM model having 26 elements and 77 degrees-of-freedom gave similar results as the low-dimensional (one degree-of-freedom) non-linear oscillator, which was developed by constructing a so-called invariant manifold. The comparisons of the dynamical responses were made in terms of time histories, phase portraits and mode shapes. (C) 2008 Elsevier Ltd. All rights reserved.

Magnetic suspension of the rotor of a ventricular assist device of mixed flow type

This work presents results of preliminary studies concerning application of magnetic bearing in a ventricular assist device (VAD) being developed by Dante Pazzanese Institute of Cardiology-IDPC (Sao Paulo, Brazil). The VAD-IDPC has a novel architecture that distinguishes from other known VADs. In this, the rotor has a conical geometry with spiral impellers, showing characteristics that are intermediate between a centrifugal VAD and an axial VAD. The effectiveness of this new type of blood pumping principle was showed by tests and by using it in heart surgery for external blood circulation. However, the developed VAD uses a combination of ball bearings and mechanical seals, limiting the life for some 10 h, making impossible its long-term use or its use as an implantable VAD. As a part of development of an implantable VAD, this work aims at the replacement of ball bearings by a magnetic bearing. The most important magnetic bearing principles are studied and the magnetic bearing developed by Escola Politecnica of Sao Paulo University (EPUSP-MB) is elected because of its very simple architecture. Besides presenting the principle of the EPUSP-MB, this work presents one possible alternative for applying the EPUSP-MB in the IDPC-VAD.

Linear minimum mean square filter for discrete-time linear systems with Markov jumps and multiplicative noises

In this paper we obtain the linear minimum mean square estimator (LMMSE) for discrete-time linear systems subject to state and measurement multiplicative noises and Markov jumps on the parameters. It is assumed that the Markov chain is not available. By using geometric arguments we obtain a Kalman type filter conveniently implementable in a recurrence form. The stationary case is also studied and a proof for the convergence of the error covariance matrix of the LMMSE to a stationary value under the assumption of mean square stability of the system and ergodicity of the associated Markov chain is obtained. It is shown that there exists a unique positive semi-definite solution for the stationary Riccati-like filter equation and, moreover, this solution is the limit of the error covariance matrix of the LMMSE. The advantage of this scheme is that it is very easy to implement and all calculations can be performed offline. (c) 2011 Elsevier Ltd. All rights reserved.

Metal separation from mixed types of batteries using selective precipitation and liquid-liquid extraction techniques

The purpose of this paper is to study metal separation from a sample composed of a mixture of the main types of spent household batteries, using a hydrometallurgical route, comparing selective precipitation and liquid-liquid extraction separation techniques. The preparation of the solution consisted of: grinding the waste of mixed batteries, reduction and volatile metals elimination using electric furnace and acid leaching. From this solution two different routes were studied: selective precipitation with sodium hydroxide and liquid-liquid extraction using Cyanex 272 [bis(2,4,4-trimethylpentyl) phosphoric acid] as extracting agent. The best results were obtained from liquid-liquid extraction in which Zn had a 99% extraction rate at pH 2.5. More than 95% Fe was extracted at pH 7.0, the same pH at which more than 90% Ce was extracted. About 88% Mn, Cr and Co was extracted at this pH. At pH 3.0, more than 85% Ni was extracted, and at pH 3.5 more than 80% of Cd and La was extracted. (C) 2010 Elsevier Ltd. All rights reserved.

Formulating Mixed Models for Experiments, Including Longitudinal Experiments

Mixed models have become important in analyzing the results of experiments, particularly those that require more complicated models (e.g., those that involve longitudinal data). This article describes a method for deriving the terms in a mixed model. Our approach extends an earlier method by Brien and Bailey to explicitly identify terms for which autocorrelation and smooth trend arising from longitudinal observations need to be incorporated in the model. At the same time we retain the principle that the model used should include, at least, all the terms that are justified by the randomization. This is done by dividing the factors into sets, called tiers, based on the randomization and determining the crossing and nesting relationships between factors. The method is applied to formulate mixed models for a wide range of examples. We also describe the mixed model analysis of data from a three-phase experiment to investigate the effect of time of refinement on Eucalyptus pulp from four different sources. Cubic smoothing splines are used to describe differences in the trend over time and unstructured covariance matrices between times are found to be necessary.

Log-modified Weibull regression models with censored data: Sensitivity and residual analysis

This paper proposes a regression model considering the modified Weibull distribution. This distribution can be used to model bathtub-shaped failure rate functions. Assuming censored data, we consider maximum likelihood and Jackknife estimators for the parameters of the model. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and we also present some ways to perform global influence. Besides, for different parameter settings, sample sizes and censoring percentages, various simulations are performed and the empirical distribution of the modified deviance residual is displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended for a martingale-type residual in log-modified Weibull regression models with censored data. Finally, we analyze a real data set under log-modified Weibull regression models. A diagnostic analysis and a model checking based on the modified deviance residual are performed to select appropriate models. (c) 2008 Elsevier B.V. All rights reserved.

Double generalized linear model for tissue culture proportion data: a Bayesian perspective

Joint generalized linear models and double generalized linear models (DGLMs) were designed to model outcomes for which the variability can be explained using factors and/or covariates. When such factors operate, the usual normal regression models, which inherently exhibit constant variance, will under-represent variation in the data and hence may lead to erroneous inferences. For count and proportion data, such noise factors can generate a so-called overdispersion effect, and the use of binomial and Poisson models underestimates the variability and, consequently, incorrectly indicate significant effects. In this manuscript, we propose a DGLM from a Bayesian perspective, focusing on the case of proportion data, where the overdispersion can be modeled using a random effect that depends on some noise factors. The posterior joint density function was sampled using Monte Carlo Markov Chain algorithms, allowing inferences over the model parameters. An application to a data set on apple tissue culture is presented, for which it is shown that the Bayesian approach is quite feasible, even when limited prior information is available, thereby generating valuable insight for the researcher about its experimental results.

The influence of environmental variables on capybara (Hydrochoerus hydrochaeris: Rodentia, Hydrochoeridae) detectability in anthropogenic environments of southeastern Brazil

Capybaras were monitored weekly from 1998 to 2006 by counting individuals in three anthropogenic environments (mixed agricultural fields, forest and open areas) of southeastern Brazil in order to examine the possible influence of environmental variables (temperature, humidity, wind speed, precipitation and global radiation) on the detectability of this species. There was consistent seasonality in the number of capybaras in the study area, with a specific seasonal pattern in each area. Log-linear models were fitted to the sample counts of adult capybaras separately for each sampled area, with an allowance for monthly effects, time trends and the effects of environmental variables. Log-linear models containing effects for the months of the year and a quartic time trend were highly significant. The effects of environmental variables on sample counts were different in each type of environment. As environmental variables affect capybara detectability, they should be considered in future species survey/monitoring programs.

ECONOMICAL OCCUPATION OF AGRICULTURAL AREA OF PIRACICABA (SAO PAULO STATE, BRAZIL), INCLUDING RISK THROUGH LINEAR PROGRAMMING

The economic occupation of an area of 500 ha for Piracicaba was studied with the irrigated cultures of maize, tomato, sugarcane and beans, having used models of deterministic linear programming and linear programming including risk for the Target-Motad model, where two situations had been analyzed. In the deterministic model the area was the restrictive factor and the water was not restrictive for none of the tested situations. For the first situation the gotten maximum income was of R$ 1,883,372.87 and for the second situation it was of R$ 1,821,772.40. In the model including risk a producer that accepts risk can in the first situation get the maximum income of R$ 1,883,372. 87 with a minimum risk of R$ 350 year(-1), and in the second situation R$ 1,821,772.40 with a minimum risk of R$ 40 year(-1). Already a producer averse to the risk can get in the first situation a maximum income of R$ 1,775,974.81 with null risk and for the second situation R$ 1.707.706, 26 with null risk, both without water restriction. These results stand out the importance of the inclusion of the risk in supplying alternative occupations to the producer, allowing to a producer taking of decision considered the risk aversion and the pretension of income.

Cow/calf preweaning efficiency of Nellore and Bos taurus x Bos indicus crosses

The objectives of this study were to determine if percentage Bos taurus (0 or 50%) of the cow had an effect on ME requirements and milk production, and to compare cow/calf efficiency among 3 mating systems. Metabolizable energy requirements were estimated during a feeding trial that encompassed a gestation and lactation feeding trial for each of 2 groups of cows. Cows were 0 or 50% Bos taurus ( 100 or 50% Nellore) breed type: Nellore cows (NL; n = 10) mated to Nellore bulls, NL cows ( n = 9) mated to Angus bulls, Angus x Nellore (ANL; n = 10) and Simmental x Nellore (SNL; n = 10) cows mated to Canchim (5/ 8 Charolais 3/ 8 Zebu) bulls. Cows were individually fed a total mixed diet that contained 11.3% CP and 2.23 Mcal of ME/kg of DM. At 14-d intervals, cows and calves were weighed and the amount of DM was adjusted to keep shrunk BW and BCS of cows constant. Beginning at 38 d of age, corn silage was available to calves ad libitum. Milk production at 42, 98, 126, and 180 d postpartum was measured using the weigh-suckle-weigh technique. At 190 d of age, calves were slaughtered and body composition estimated using 9-10-11th-rib section to obtain energy deposition. Regression of BW change on daily ME intake (MEI) was used to estimate MEI at zero BW change. Increase in percentage Bos taurus had a significant effect on daily ME requirements (Mcal/d) during pregnancy (P < 0.01) and lactation (P < 0.01). Percentage Bos taurus had a positive linear effect on maintenance requirements of pregnant (P = 0.07) and lactating (P < 0.01) cows; during pregnancy, the ME requirements were 91 and 86% of those in lactation (131 +/- 3.5 vs. 145 +/- 3.4 Mcal.kg(-0.75).d(-1)) for the 0 and 50% B. taurus groups, respectively. The 50% B. taurus cows, ANL and SNL, suckling crossbred calves had greater total MEI (4,319 +/- 61 Mcal; P < 0.01) than 0% B. taurus cows suckling NL (3,484 +/- 86 Mcal) or ANL calves (3,600 +/- 91 Mcal). The 0% B. taurus cows suckling ANL calves were more efficient (45.3 +/- 1.6 g/Mcal; P = 0.03) than straightbred NL (35.1 +/- 1.5 g/Mcal) and ANL or SNL pairs (41.0 +/- 1.0 g/Mcal). Under the conditions of this study, crossbreeding improved cow/ calf efficiency and showed an advantage for cows that have lower energy requirements.

A Log-Linear Regression Model for the Beta-Weibull Distribution

We introduce the log-beta Weibull regression model based on the beta Weibull distribution (Famoye et al., 2005; Lee et al., 2007). We derive expansions for the moment generating function which do not depend on complicated functions. The new regression model represents a parametric family of models that includes as sub-models several widely known regression models that can be applied to censored survival data. We employ a frequentist analysis, a jackknife estimator, and a parametric bootstrap for the parameters of the proposed model. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess global influences. Further, for different parameter settings, sample sizes, and censoring percentages, several simulations are performed. In addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be extended to a modified deviance residual in the proposed regression model applied to censored data. We define martingale and deviance residuals to evaluate the model assumptions. The extended regression model is very useful for the analysis of real data and could give more realistic fits than other special regression models.