Vicente Rico-Ramirez

MINLP synthesis of heat exchanger networks considering pressure drop effects

Considerable research effort has been reported in both Pinch Technology and MINLP techniques for the synthesis of heat exchanger networks. However, most of the design procedures assume constant stream heat transfer coefficients. A problem arises because there is no guarantee that the values of the coefficients assumed during network synthesis are the same as those actually achieved in detailed equipment design (Trans. IChemE 69 (1991) 445). It has been shown that network synthesis and detailed exchanger design can be made consistent if network synthesis is based on allowable pressure drops rather than on assumed film coefficients. Motivated by earlier applications in pinch technology, in this work we extend the simultaneous MINLP model for the design of heat exchanger networks (Comput. Chem. Eng. 14 (1990) 1165) by removing the assumption of constant film heat transfer coefficients and incorporating instead the effect of allowable pressure drop. Numerical results in two illustrative examples are shown to demonstrate the potential benefits and scope of the proposed approach.

Water networks security: A two-stage mixed-integer stochastic program for sensor placement under uncertainty

This work describes a stochastic approach for the optimal placement of sensors in municipal water networks to detect maliciously injected contaminants. The model minimizes the expected fraction of the population at risk and the cost of the sensors. Our work explicitly includes uncertainties in the attack risk and population density, so that the resulting problem involves optimization under uncertainty. In our formulation, we include the location of a number of sensors as first stage decision variables of a two-stage mixed-integer stochastic linear problem; the second stage evaluates the population at risk for the scenario obtained in the first stage and that information is then used to modify the first stage decisions for the next iteration. Since the model is integer in the first stage, a generalized framework based on the stochastic decomposition algorithm allows us to solve the problem in a reasonable computational time. The paper describes the mixed-integer stochastic model and the algorithmic framework, and compares the deterministic and stochastic optimal solutions. The network used as our case study has been derived through the water network simulator EPANET 1.0; four acyclic water flow patterns are considered. Results show a significant effect of uncertainty in sensor placement and total cost.

Stochastic maximum principle for optimal control under uncertainty

Abstract Optimal control problems involve the difficult task of determining time-varying profiles through dynamic optimization. Such problems become even more complex in practical situations where handling time dependent uncertainties becomes an important issue. Approaches to stochastic optimal control problems have been reported in the finance literature and are based on real option theory, combining Ito’s Lemma and the dynamic programming formulation. This paper describes a new approach to stochastic optimal control problems in which the stochastic dynamic programming formulation is converted into a stochastic maximum principle formulation. An application of such method has been reported by Rico-Ramirez et al. ( Computers and Chemical Engineering, 2003, 27, 1867 ) but no details of the derivation were provided. The main significance of this approach is that the solution to the partial differential equations involved in the dynamic programming formulation is avoided. The classical isoperimetric problem illustrates this approach.

Simulation study on biodiesel production by reactive distillation with methanol at high pressure and temperature: Impact on costs and pollutant emissions

a b s t r a c t Recently, a two-step biodiesel production process which uses short-chain alcohols at supercritical con- ditions has been proposed. In addition, literature reports suggest that the COSMO-SAC thermodynamic model is a suitable alternative for the prediction of VLE for supercritical methanol/methyl esters mix- tures. Thus, in this work a simulation study of the two-step supercritical method for the production of biodiesel is performed by using the COSMO-SAC model. Further, alternative system configurations for biodiesel production based on reactive distillation are proposed and their total emissions are compared to those corresponding to the conventional catalytic method. The study demonstrates the benefits of using reactive distillation for the esterification step and discusses the environmental impact of the supercritical production process. It has been found that the intensified alternatives reduce the emissions considerably and, through the reuse of the excess methanol, the emissions level of the supercritical process can be compared to those of the catalytic method.

An MFA optimization approach for pollution trading considering the sustainability of the surrounded watersheds

Abstract This paper proposes a mathematical programming model for the pollution trading among different pollution sources which considers the sustainability of the surrounding watershed. The formulation involves the minimization of the costs associated to the implementation of the required technology to satisfy the environmental constraints in order to achieve optimal water quality conditions. The model uses a material flow analysis technique to represent changes on the behavior of the watershed due to the polluted discharges. The material flow analysis considers all discharges and extractions (i.e., industrial and residential discharges, pluvial precipitation, evaporation, etc.) as well as the chemical and biochemical reactions taking place in the watershed. In the context of pollution trading, the implementation of the proposed formulation determines if an industrial source must buy credits to compensate the violation of environmental constraints, or if it requires the installation of treatment technologies to sell credits to another source. The formulation was applied to a case study involving the drainage system of the Bahr El-Baqar region in Egypt; the results show the advantages of the proposed approach in terms of cost and sustainability.

Improving convergence of the stochastic decomposition algorithm by using an efficient sampling technique

This work focuses on the basic stochastic decomposition (SD) algorithm of Higle and Sen [J.L. Higle, S. Sen, Stochastic Decomposition, Kluwer Academic Publishers, 1996] for two-stage stochastic linear programming problems with complete recourse. The algorithm uses sampling when the random variables are represented by continuous distribution functions. Traditionally, this method has been applied by using Monte Carlo (MC) sampling to generate the samples of the stochastic variables. However, Monte Carlo methods can result in large error bounds and variance. Hence, some other approaches use importance sampling to reduce variance and achieving convergence faster that the method based on the Monte Carlo sampling technique. This work proposes an improvement on this respect. Hence, we propose to replace the use of the Monte Carlo sampling technique or the importance sampling in the SD algorithm by the use of the novel Hammersley sequence sampling (HSS) technique. Recently, such a technique has proved to provide better uniformity properties than other sampling techniques and, as a consequence, the variance and the number of samples required for convergence are reduced. Also, we use a fractal dimension approach to characterize the error of the estimation of the recourse function based on sampling. The computational implementation of the algorithm involves a framework that integrates the GAMS modeling environment, the HSS sampling code (FORTRAN) and a C++ program which generates appropriate LP problems for each SD iteration. The algorithm has been tested with several case studies representing chemical engineering applications and the results are discussed.

Stochastic Optimal Control for the Treatment of a Pathogenic Disease

Abstract Mathematical modeling as a tool for the treatment of a pathogenic disease has been widely proposed in the literature. Most of the modeling approaches represent the immune system dynamics as deterministic optimal control problems. Deterministic approaches, however, do not consider uncertainties in model parameters and variability among different individuals. To include uncertainties in the formulation, the aim of this paper has been using stochastic optimal control theory to develop protocols for the treatment of human diseases. We model time dependent uncertainties as Ito processes. That results in an optimal control problem where the constraints are stochastic differential equations and the objective function is an integral equation. The optimality conditions of the problem are obtained through the stochastic maximum principle, which results in a boundary value problem. The boundary value problem is solved iteratively by using a combination of the gradient method and a stochastic version of the Runge-Kutta method derived in this work. As an illustration of the proposed approach, we solve a mathematical model to determine the evolution of a generic disease and obtain regimens for applying therapeutic agents in a manner that maximizes efficacy while minimizing side effects. We show that stochastic optimal control theory can indeed help develop clinical insight in treating illness under uncertainties in model parameters.

A comparative simulation study of power generation plants involving chemical looping combustion systems

Abstract This work presents a simulation study on both energy and economics of power generation plants with inherent CO 2 capture based on chemical looping combustion technologies. Combustion systems considered include a conventional chemical looping system and two extended three-reactor alternatives (exCLC and CLC3) for simultaneous hydrogen production. The power generation cycles include a combined cycle with steam injected gas turbines, a humid air turbine cycle and a simple steam cycle. Two oxygen carriers are considered in our study, iron and nickel. We further analyze the effect of the pressure reaction and the turbine inlet temperature on the plant efficiency. Results show that plant efficiencies as high as 54% are achieved by the chemical looping based systems with competitive costs. That value is well above the efficiency of 46% obtained by a conventional natural gas combined cycle system under the same conditions and simulation assumptions.

Implementation of a reactive dividing wall distillation column in a pilot plant

Abstract Based on the knowledge regarding steady state design, optimization and control obtained by using Aspen Plus and Aspen Dynamics process simulators, we have designed and implemented a reactive dividing wall distillation column (DWDC). The column can be used to carry out the equilibrium reaction between ethanol and acetic acid to produce ethyl acetate and water catalyzed by sulfuric acid. The reactive DWDC contains three packed sections and the middle section is the key part in order to minimize the energy consumption. That section contains a wall that can be moved to three positions to manipulate the split of the vapor stream, whereas the split of the liquid stream is achieved by using a side tank. The reactive DWDC contains a reflux valve used to control either the composition of the distillate or the temperature at some point in the first packed section. Also, a reboiler was implemented in the lower section, and the heat duty supplied to it is used to control either the composition of the bottoms product or the temperature in the reboiler. This design was proposed based on both steady and dynamic simulations. The minimum energy consumption was predicted in the steady state simulation; the dynamic simulations indicated that the minimum energy consumption can be achieved in practice by implementing two control loops of temperature (or composition), as described.

Gas chromatography/mass spectrometry for the determination of nitrosamines in red wine.

N-nitrosamines (NAms) are highly active carcinogens that have been detected in food and beverages. Currently certain studies report their presence in red wine, while others fail to detect their presence. In this study the head space solid phase micro-extraction technique coupled to gas chromatography-mass spectrometry (HS-SPME-GC-MS) was applied to quantify four NAms in different types of red wine. The technique was found to be a simple, precise, fast and environmentally friendly alternative for the quantification of volatile NAms. A factorial analysis was carried out to evaluate the influence of the parameters on the HS-SPME technique. This is the first study that such analysis has been reported and where NAms in red wine have been quantified using HS-SPME-GC-MS. The method was validated by calculating the linearity, limit of detection and quantification. Two of the four NAms analyzed were found to be present in red wine samples.