Luis A. Ricardez-Sandoval

Pyrrolic-structure enriched nitrogen doped graphene for highly efficient next generation supercapacitors

This study reports the preparation of pyrrolic-structure enriched nitrogen doped graphene by hydrothermal synthesis at varied temperature. The morphology, structure and composition of the prepared nitrogen doped graphene were confirmed with SEM, XRD, XPS and Raman spectroscopy. The material was tested for supercapacitive behavior. It was found that doping graphene with nitrogen increased the electrical double layer supercapacitance to as high as 194 F g−1. Furthermore, density functional theory (DFT) calculations showed the proper level of binding energy found between the pyrrolic-nitrogen structure and the electrolyte ions, which may be used to explain the highest contribution of the pyrrolic-structure to the capacitance.

Integration of design and control for chemical processes: A review of the literature and some recent results ☆

Abstract This paper presents a literature review on the integration of control and design problem followed by the description of two new methodologies that have been recently applied to achieve this integration. These methods are based on mathematical tools that have been commonly used for the design of robust controllers. Using these tools, the integration of the control and design problem can be formulated as a nonlinear constrained optimization problem that is significantly less computationally demanding than previously proposed dynamic optimization-based optimization methods. A mixing tank process is used to illustrate the proposed methodologies. Part of the material included in this manuscript was presented as a keynote lecture at the DYCOPS 2007 conference ( Ricardez Sandoval et al., 2007 ).

Optimal design and control of dynamic systems under uncertainty: A probabilistic approach

Abstract This paper presents a new methodology for the simultaneous design and control of systems under random realizations in the disturbances. The key idea in this work is to perform a distribution analysis on the worst-case variability. Normal distribution functions, which approximate the actual distribution of the worst-case variability, are used to estimate the largest variability expected for the process variables at a user-defined probability limit. The resulting estimates in the worst-case variability are used to evaluate the process constraints, the systems dynamic performance and the process economics. The methodology was applied to simultaneously design and control a Continuous Stirred Tank Reactor (CSTR) process. A study on the computational demands required by the present method is presented and compared with a dynamic optimization-based methodology. The results show that the present methodology is a computationally efficient and practical tool that can be used to propose attractive (economical) process designs under uncertainty.

A methodology for the simultaneous design and control of large-scale systems under process parameter uncertainty

This work presents a simultaneous design and control methodology for large-scale systems. The approach is based on the identification of an uncertain model from a first-principle process model. Using the identified uncertain model, a Structured Singular Value (SSV) analysis is used to estimate the realizations in the disturbance set that generates the worst-case variability and constraint violations. Then, simulations of the first-principle process model are performed with the critical disturbance profile as input to estimate the actual worst-case output variability and the worst-case variations in the process constraints. Since the proposed methodology is formulated as a nonlinear constrained optimization problem, it avoids the computationally expensive task of solving dynamic optimization problems, making it suitable for application to large-scale systems. The proposed methodology was tested on the Tennessee Eastman process to show that a redesign of the major process units in the process could significantly reduce the costs of this plant.

Simultaneous design and MPC-based control for dynamic systems under uncertainty: A stochastic approach

Abstract A stochastic-based simultaneous design and control methodology for chemical processes under uncertainty is presented. An optimization framework is proposed with the aim of achieving a feasible and stable optimal process design in the presence of stochastic disturbances while using advanced model-based control schemes such as Model Predictive Control (MPC). The key idea is to determine the dynamic variability of the system that will be accounted for in the process design using a stochastic-based worst-case variability index. This index is computed from the probability distribution of the worst-case variability of the process variables that determine the dynamic feasibility or the dynamic performance of the system under random realizations in the disturbances. A case study of an actual wastewater treatment industrial plant is presented and used to test the proposed methodology and compare its performance against the sequential design approach and a simultaneous design and control method using conventional PI-based control schemes.

An MPC-based control structure selection approach for simultaneous process and control design

Abstract An optimization framework that addresses the simultaneous process and control design of chemical systems including the selection of the control structure is presented. Different control structures composed of centralized and fully decentralized predictive controllers are considered in the analysis. The systems dynamic performance is quantified using a variability cost function that assigns a cost to the worst-case closed-loop variability, which is calculated using analytical bounds derived from tests used for robust control design. The selection of the controller structure is based on a communication cost term that penalizes pairings between the manipulated and the controlled variables based on the tuning parameters of the MPC controller and the process gains. Both NLP and MINLP formulations are proposed. The NLP formulation is shown to be faster and converges to a similar solution to that obtained with the MINLP formulation. The proposed methods were applied to a wastewater treatment industrial plant.

Multi-scenario modelling of uncertainty in stochastic chemical systems

Uncertainty analysis has not been well studied at the molecular scale, despite extensive knowledge of uncertainty in macroscale systems. The ability to predict the effect of uncertainty allows for robust control of small scale systems such as nanoreactors, surface reactions, and gene toggle switches. However, it is difficult to model uncertainty in such chemical systems as they are stochastic in nature, and require a large computational cost. To address this issue, a new model of uncertainty propagation in stochastic chemical systems, based on the Chemical Master Equation, is proposed in the present study. The uncertain solution is approximated by a composite state comprised of the averaged effect of samples from the uncertain parameter distributions. This model is then used to study the effect of uncertainty on an isomerization system and a two gene regulation network called a repressilator. The results of this model show that uncertainty in stochastic systems is dependent on both the uncertain distribution, and the system under investigation.

Numerical modeling of solid-phase microextraction: binding matrix effect on equilibrium time.

Solid-phase microextraction (SPME) is a well-known sampling and sample preparation technique used for a wide variety of analytical applications. As there are various complex processes taking place at the time of extraction that influence the parameters of optimum extraction, a mathematical model and computational simulation describing the SPME process is required for experimentalists to understand and implement the technique without performing multiple costly and time-consuming experiments in the laboratory. In this study, a mechanistic mathematical model for the processes occurring in SPME extraction of analyte(s) from an aqueous sample medium is presented. The proposed mechanistic model was validated with previously reported experimental data from three different sources. Several key factors that affect the extraction kinetics, such as sample agitation, fiber coating thickness, and presence of a binding matrix component, are discussed. More interestingly, for the first time, shorter or longer equilibrium times in the presence of a binding matrix component were explained with the help of an asymptotic analysis. Parameters that contribute to the variation of the equilibrium times are discussed, with the assumption that one binding matrix component is present in a static sample. Numerical simulation results show that the proposed model captures the phenomena occurring in SPME, leading to a clearer understanding of this process. Therefore, the currently presented model can be used to identify optimum experimental parameters without the need to perform a large number of experiments in the laboratory.

Simultaneous design and control under uncertainty: A back-off approach using power series expansions

Abstract A methodology for simultaneous design and control for dynamic systems under uncertainty has been developed. The algorithm moves away (back-off) from the optimal steady-state design (which is often found to be dynamically infeasible) to a new feasible operating point under process dynamics and parameter uncertainty by solving a set of optimization problems in an iterative manner. Power Series Expansions (PSE) are employed to represent the cost function and constraints in the optimization problems. The challenge in this method is to calculate in a systematic fashion the amount of back-off needed to accommodate the transient operation of the process. The method has been tested on an isothermal storage tank and a waste water treatment plant and the results compared with the formal integration. The results have shown that this method has the potential to address the simultaneous design and control of dynamic systems under uncertainty at lower computational costs.

A dynamic optimization framework for integration of design, control and scheduling of multi-product chemical processes under disturbance and uncertainty

Abstract A novel dynamic optimization framework is presented for integration of design, control, and scheduling for multi-product processes in the presence of disturbances and parameter uncertainty. This framework proposes an iterative algorithm that decomposes the overall problem into flexibility and feasibility analyses. The flexibility problem is solved under a critical (worst-case) set of disturbance and uncertainty realizations, whereas the feasibility problem evaluates the dynamic feasibility of each realization, and updates the critical set accordingly. The algorithm terminates when a robust solution is found, which is feasible under all identified scenarios. To account for the importance of grade transitions in multiproduct processes, the proposed framework integrates scheduling into the dynamic model by the use of flexible finite elements. This framework is applied to a multi-product continuous stirred-tank reactor (CSTR) system subject to disturbance and parameter uncertainty. The proposed method is shown to return robust solutions that are of higher quality than the traditional sequential method. The results indicate that scheduling decisions are affected by design and control decisions, thus motivating the need for integration of these three aspects.