Coordinate regulation of gonadotropin-releasing hormone neuronal firing patterns by cytosolic calcium and store depletion

Elevation of cytosolic free Ca2+ concentration ([Ca2+]i) in excitable cells often acts as a negative feedback signal on firing of action potentials and the associated voltage-gated Ca2+ influx. Increased [Ca2+]i stimulates Ca2+-sensitive K+ channels (IK-Ca), and this, in turn, hyperpolarizes the cell and inhibits Ca2+ influx. However, in some cells expressing IK-Ca the elevation in [Ca2+]i by depletion of intracellular stores facilitates voltage-gated Ca2+ influx. This phenomenon was studied in hypothalamic GT1 neuronal cells during store depletion caused by activation of gonadotropin-releasing hormone (GnRH) receptors and inhibition of endoplasmic reticulum (Ca2+)ATPase with thapsigargin. GnRH induced a rapid spike increase in [Ca2+]i accompanied by transient hyperpolarization, followed by a sustained [Ca2+]i plateau during which the depolarized cells fired with higher frequency. The transient hyperpolarization was caused by the initial spike in [Ca2+]i and was mediated by apamin-sensitive IK-Ca channels, which also were operative during the subsequent depolarization phase. Agonist-induced depolarization and increased firing were independent of [Ca2+]i and were not mediated by inhibition of K+ current, but by facilitation of a voltage-insensitive, Ca2+-conducting inward current. Store depletion by thapsigargin also activated this inward depolarizing current and increased the firing frequency. Thus, the pattern of firing in GT1 neurons is regulated coordinately by apamin-sensitive SK current and store depletion-activated Ca2+ current. This dual control of pacemaker activity facilitates voltage-gated Ca2+ influx at elevated [Ca2+]i levels, but also protects cells from Ca2+ overload. This process may also provide a general mechanism for the integration of voltage-gated Ca2+ influx into receptor-controlled Ca2+ mobilization.

Real time optimization in chemical process: evaluation of strategies, improvements and industrial application.

The increasing economic competition drives the industry to implement tools that improve their processes efficiencies. The process automation is one of these tools, and the Real Time Optimization (RTO) is an automation methodology that considers economic aspects to update the process control in accordance with market prices and disturbances. Basically, RTO uses a steady-state phenomenological model to predict the process behavior, and then, optimizes an economic objective function subject to this model. Although largely implemented in industry, there is not a general agreement about the benefits of implementing RTO due to some limitations discussed in the present work: structural plant/model mismatch, identifiability issues and low frequency of set points update. Some alternative RTO approaches have been proposed in literature to handle the problem of structural plant/model mismatch. However, there is not a sensible comparison evaluating the scope and limitations of these RTO approaches under different aspects. For this reason, the classical two-step method is compared to more recently derivative-based methods (Modifier Adaptation, Integrated System Optimization and Parameter estimation, and Sufficient Conditions of Feasibility and Optimality) using a Monte Carlo methodology. The results of this comparison show that the classical RTO method is consistent, providing a model flexible enough to represent the process topology, a parameter estimation method appropriate to handle measurement noise characteristics and a method to improve the sample information quality. At each iteration, the RTO methodology updates some key parameter of the model, where it is possible to observe identifiability issues caused by lack of measurements and measurement noise, resulting in bad prediction ability. Therefore, four different parameter estimation approaches (Rotational Discrimination, Automatic Selection and Parameter estimation, Reparametrization via Differential Geometry and classical nonlinear Least Square) are evaluated with respect to their prediction accuracy, robustness and speed. The results show that the Rotational Discrimination method is the most suitable to be implemented in a RTO framework, since it requires less a priori information, it is simple to be implemented and avoid the overfitting caused by the Least Square method. The third RTO drawback discussed in the present thesis is the low frequency of set points update, this problem increases the period in which the process operates at suboptimum conditions. An alternative to handle this problem is proposed in this thesis, by integrating the classic RTO and Self-Optimizing control (SOC) using a new Model Predictive Control strategy. The new approach demonstrates that it is possible to reduce the problem of low frequency of set points updates, improving the economic performance. Finally, the practical aspects of the RTO implementation are carried out in an industrial case study, a Vapor Recompression Distillation (VRD) process located in Paulínea refinery from Petrobras. The conclusions of this study suggest that the model parameters are successfully estimated by the Rotational Discrimination method; the RTO is able to improve the process profit in about 3%, equivalent to 2 million dollars per year; and the integration of SOC and RTO may be an interesting control alternative for the VRD process.

Some examples of modeling in chemical engineering: thermal treatment of polymers, phase equilibrium calculations and process design

Presentation submitted to PSE Seminar, Chemical Engineering Department, Center for Advanced Process Design-making (CAPD), Carnegie Mellon University, Pittsburgh (USA), October 2012.

Simultaneous synthesis of heat exchanger networks with pressure recovery: optimal integration between heat and work

The optimal integration between heat and work may significantly reduce the energy demand and consequently the process cost. This paper introduces a new mathematical model for the simultaneous synthesis of heat exchanger networks (HENs) in which the pressure levels of the process streams can be adjusted to enhance the heat integration. A superstructure is proposed for the HEN design with pressure recovery, developed via generalized disjunctive programming (GDP) and mixed-integer nonlinear programming (MINLP) formulation. The process conditions (stream temperature and pressure) must be optimized. Furthermore, the approach allows for coupling of the turbines and compressors and selection of the turbines and valves to minimize the total annualized cost, which consists of the operational and capital expenses. The model is tested for its applicability in three case studies, including a cryogenic application. The results indicate that the energy integration reduces the quantity of utilities required, thus decreasing the overall cost.

Functional properties of sodium and calcium caseinate antimicrobial active films containing carvacrol

Active edible films were prepared by adding carvacrol into sodium caseinate (SC) and calcium caseinate (CC) matrices plasticized with two different glycerol concentrations (25 and 35 wt%) prepared by solvent casting. Functional characterisation of these bio-films was carried out by determination of some of their physico-chemical properties, such as colour, transparency, oxygen barrier, wettability, dye permeation properties and antibacterial effectiveness against Gram negative and Gram positive bacteria. All films exhibited good performance in terms of optical properties in the CIELab space showing high transparency. Carvacrol was able to reduce CC oxygen permeability and slightly increased the surface hydrophobicity. Dye diffusion experiments were performed to evaluate permeation properties. The diffusion of dye through films revealed that SC was more permeable than CC. The agar diffusion method was used for the evaluation of the films antimicrobial effectiveness against Escherichia coli and Staphylococcus aureus. Both SC and CC edible films with carvacrol showed inhibitory effects on both bacteria.

Integration of different models in the design of chemical processes: Application to the design of a power plant

With advances in the synthesis and design of chemical processes there is an increasing need for more complex mathematical models with which to screen the alternatives that constitute accurate and reliable process models. Despite the wide availability of sophisticated tools for simulation, optimization and synthesis of chemical processes, the user is frequently interested in using the ‘best available model’. However, in practice, these models are usually little more than a black box with a rigid input–output structure. In this paper we propose to tackle all these models using generalized disjunctive programming to capture the numerical characteristics of each model (in equation form, modular, noisy, etc.) and to deal with each of them according to their individual characteristics. The result is a hybrid modular–equation based approach that allows synthesizing complex processes using different models in a robust and reliable way. The capabilities of the proposed approach are discussed with a case study: the design of a utility system power plant that has been decomposed into its constitutive elements, each treated differently numerically. And finally, numerical results and conclusions are presented.