Juan Manuel González Gonzalez

Comparison of transient elastography and liver biopsy for the assessment of liver fibrosis in HIV/hepatitis C virus-coinfected patients and correlation with noninvasive serum markers.

Summary.  Transient elastography (FibroScan®) is a novel, rapid and noninvasive technique to assess liver fibrosis. Our objective was to compare transient elastography (TE) and other noninvasive serum indexes as alternatives to liver biopsy in HIV/hepatitis C virus (HCV)‐coinfected patients. The fibrosis stage (METAVIR Score), TE, the aspartate aminotransferase‐to‐platelet ratio index, the Forns fibrosis index, FIB‐4 and HGM‐2 indexes were assessed in 100 patients between January 2007 and January 2008. The diagnostic values were compared by calculating the area under the receiver operating characteristic curves (AUROCs). Using TE, the AUROC (95% CI) of liver stiffness was 0.80 (0.72–0.89) when discriminating between F ≤ 1 and F > 2, 0.93 (0.85–1.00) when discriminating between F ≤ 2 and F > 3 and 0.99 (0.97–1.00) when discriminating between F ≤ 3 and F4. For the diagnosis of F ≥ 3, the AUROCs of TE were significantly higher than those obtained with the other four noninvasive indexes. Based on receiver operating characteristic curves, three cutoff values were chosen to identify F ≤ 1 (<7 kPa), F ≥ 3 (≥11 kPa) and F4 (≥14 kPa). Using these best cutoff scores, the negative predictive value and positive predictive value were 81.1% and 70.2% for the diagnosis of F ≤ 1, 96.3% and 60% for the diagnosis of F ≥ 3 and 100% and 57.1% for the diagnosis of F4. Thus, Transient elastography accurately predicted liver fibrosis and outperformed other simple noninvasive indexes in HIV/HCV‐coinfected patients. Our data suggest that TE is a helpful tool for guiding therapeutic decisions in clinical practice.

Robust PI2 controller for continuous bioreactors

Abstract An estimation algorithm related to the kinetics terms of the main uncertainties present in a continuous stirred tank bioreactor, is developed. This algorithm is based on a proportional–integral reduced order uncertainty observer. With the estimate generated of the uncertain term, an input–output linearizing feedback control is designed which provides robust regulation model uncertainties, noisy measurements and sustained disturbances. This control strategy could be represented as PI 2 controller where new tuning rules of the controller gains are given in terms of the estimation and closed-loop time constants. The performance of the estimation algorithm and the corresponding closed-loop behaviour of the system are compared to a standard PI controller and they are illustrated by means of numerical simulations.

Sliding mode observer-based control for a class of bioreactors

Abstract A robust control algorithm which uses partial state feedback is designed for a class of biochemical processes in the presence of modeling uncertainties. To design the controller, the model uncertainty and the nonmeasurable state are combined into a new state variable. A sliding mode state observer is used to obtain on-line estimates of this new state. A practical stabilizer is obtained by combining the observer with an input–output linearizing controller. The practical convergence of the observer and the controller are proved. The performance of the sliding mode observer and the closed-loop behavior is illustrated through numerical simulations.

Model-based control of mold level in a continuous steel caster under model uncertainties

Abstract In tackling the problem of liquid steel level regulation in the mold of a continuous steel caster, the values of some system parameters, such as the clogging degree and the discharge coefficients of nozzles, are seldom precisely known. The discrete control laws developed in this work employ on-line estimated values of these parameters, to produce a stable regulation of mold and tundish levels for an industrial-type two-strand continuous slab caster. The estimation procedure is delayed by one time step (the sampling time) in order to become realizable; when the sampling time is small enough, the discrete control scheme gives stability and convergence properties that are analogous to those of an ideal control scheme with accurate knowledge of the parameter values.

Nonlinear regulation for a continuous bioreactor via a numerical uncertainty observer

Abstract A robust adaptive control strategy for a class of continuous-flow bioreactor process where the intrinsic reaction rates are poorly know is provided. A numerical observer is used to obtain on-line discrete estimates of the main uncertainties (kinetics term, yield production, and biomass concentration) of a continuous bioreactor. With this estimate, a linearizing feedback control law is obtained which provides robust regulation against uncertainties, disturbances and additive noise on the substrate concentration measurements. The performance of the closedloop system is illustrated via numerical simulations.

Successful bounded control for a chemical reactor with non-linear oscillatory consecutive reactions

Abstract The closed-loop dynamic behavior of a chemical reactor with two consecutive and oscillatory exothermic reactions is analyzed. When bounds are imposed on the control input, equilibrium points induced by saturation (EPIS) are generated. In this case, the reactor dynamic trajectories are deviated towards the EPIS and their convergence to the set-point is no longer guaranteed. By means of bifurcation analysis, necessary and sufficient conditions are derived to obtain quasi-optimal regulatory control and to avoid the presence of EPIS. Simulation studies proved the advantages of implementing these conditions, giving smooth responses with no oscillation.

A strategy to regulate continuous fermentation processes with unknown reaction rates

Identification and control of continuous fermentation processes are dif-ficult tasks due to the complexity and high coupling of dynamic behaviour of this kind of system. In this work is implemented an on-line estimation technique of the main uncertainties of a fermentation processes (e.g. specific growth rate, biomass concentration and yield coefficient) based on a mass balance, to generate a linearising feedback control law that provides a robust stabilisation against uncertainties. By numerical simulations the performance of the closed-loop system and the controller design procedure is illustrated.