Optimization of Process Flowsheets through Metaheuristic Techniques

This book presents a multi-objective optimization framework for optimizing chemical processes. The proposed framework implements a link between process simulators and metaheuristic techniques. The proposed approach is general, and there can be used any process simulator and any metaheuristic technique. This book shows how to implement links between different process simulators such as Aspen Plus®, HYSYS®, SuperPro Designer®, and others, linked to metaheuristic techniques implemented in Matlab®, Excel®, C++, or other programs. This way, the proposed framework allows optimizing any process flowsheet implemented in the process simulator and using the metaheuristic technique, and this way the numerical complications through the optimization process can be eliminated. Furthermore, the proposed framework allows using the thermodynamic, design, and constitutive equations implemented in the process simulator to implement any process

Development of Biomimetic-Based Controller Design Methods for Advanced Energy Systems

A biologically inspired optimal control strategy, denoted as BIO-CS, is proposed for advanced energy systems applications. This strategy combines the ant\u27s rule of pursuit idea with multi-agent and optimal control concepts. The BIO-CS algorithm employs gradient-based optimal control solvers for the intermediate problems associated with the leader-follower agents\u27 local interactions. The developed BIO-CS is integrated with an Artificial Neural Network (ANN)-based adaptive component for further improvement of the overall framework. In particular, the ANN component captures the mismatch between the controller and the plant models by using a single-hidden-layer technique with online learning capabilities to augment the baseline BIO-CS control laws. The resulting approach is a unique combination of biomimetic control and data-driven methods that provides optimal solutions for dynamic systems.;The applicability of the proposed framework is illustrated via an Integrated Gasification Combined Cycle (IGCC) process with carbon capture as an advanced energy system example. Specifically, a multivariable control structure associated with a subsystem of the IGCC plant simulation in DYNSIMRTM software platform is addressed. The proposed control laws are derived in MATLAB RTM environment, while the plant models are built in DYNSIM RTM, and a previously developed MATLABRTM-DYNSIM RTM link is employed for implementation purposes. The proposed integrated approach improves the overall performance of the process up to 85% in terms of reducing the output tracking error when compared to stand-alone BIO-CS and Proportional-Integral (PI) controller implementations, resulting in faster setpoint tracking.;Other applications of BIO-CS addressed include: i) a nonlinear fermentation process to produce ethanol; and ii) a transfer function model derived from the cyber-physical fuel cell-gas turbine hybrid power system that is part of the Hybrid Performance (HYPER) project at the National Energy Technology Laboratory (NETL). Other theoretical developments in this work correspond to the integration of the BIO-CS approach with Multi-Agent Optimization (MAO) techniques and casting BIO-CS as a Model Predictive Controller (MPC). These developments are demonstrated by revisiting the fermentation process example. The proposed biologically-inspired approaches provide a promising alternative for advanced control of energy systems of the future

Process intensification education contributes to sustainable development goals: Part 2

Achieving the United Nations sustainable development goals requires industry and society to develop tools and processes that work at all scales, enabling goods delivery, services, and technology to large conglomerates and remote regions. Process Intensification (PI) is a technological advance that promises to deliver means to reach these goals, but higher education has yet to totally embrace the program. Here, we present practical examples on how to better teach the principles of PI in the context of the Bloom's taxonomy and summarise the current industrial use and the future demands for PI, as a continuation of the topics discussed in Part 1. In the appendices, we provide details on the existing PI courses around the world, as well as teaching activities that are showcased during these courses to aid students’ lifelong learning. The increasing number of successful commercial cases of PI highlight the importance of PI education for both students in academia and industrial staff.We acknowledge the sponsors of the Lorentz’ workshop on“Educating in PI”: The MESA+Institute of the University of Twente,Sonics and Materials (USA) and the PIN-NL Dutch Process Intensi-fication Network. DFR acknowledges support by The Netherlands Centre for Mul-tiscale Catalytic Energy Conversion (MCEC), an NWO Gravitationprogramme funded by the Ministry of Education, Culture and Sci-ence of the government of The Netherlands. NA acknowledges the Deutsche Forschungsgemeinschaft (DFG)- TRR 63¨Integrierte Chemische Prozesse in flüssigen Mehrphasen-systemen¨(Teilprojekt A10) - 56091768. The participation by Robert Weber in the workshop and thisreport was supported by Laboratory Directed Research and Devel-opment funding at Pacific Northwest National Laboratory (PNNL).PNNL is a multiprogram national laboratory operated for theUS Department of Energy by Battelle under contract DE-AC05-76RL0183

Sensor Placement Algorithms for Process Efficiency Maximization

Even though the senor placement problem has been studied for process plants, it has been done for minimizing the number of sensors, minimizing the cost of the sensor network, maximizing the reliability, or minimizing the estimation errors. In the existing literature, no work has been reported on the development of a sensor network design (SND) algorithm for maximizing efficiency of the process. The SND problem for maximizing efficiency requires consideration of the closed-loop system, which is unlike the open-loop systems that have been considered in previous works. In addition, work on the SND problem for a large fossil energy plant such as an integrated gasification combined cycle (IGCC) power plant with CO2 capture is rare.;The objective of this research is to develop a SND algorithm for maximizing the plant performance using criteria such as efficiency in the case of an estimator-based control system. The developed algorithm will be particularly useful for sensor placement in IGCC plants at the grassroots level where the number, type, and location of sensors are yet to be identified. In addition, the same algorithm can be further enhanced for use in retrofits, where the objectives could be to upgrade (addition of more sensors) and relocate existing sensors to different locations. The algorithms are developed by considering the presence of an optimal Kalman Filter (KF) that is used to estimate the unmeasured and noisy measurements given the process model and a set of measured variables. The designed algorithms are able to determine the location and type of the sensors under constraints on budget and estimation accuracy. In this work, three SND algorithms are developed: (a) steady-state SND algorithm, (b) dynamic model-based SND algorithm, and (c) nonlinear model-based SND algorithm. These algorithms are implemented in an acid gas removal (AGR) unit as part of an IGCC power plant with CO2 capture. The AGR process involves extensive heat and mass integration and therefore, is very suitable for the study of the proposed algorithm in the presence of complex interactions between process variables

MODELING THE EFFECT OF TEMPERATURE ON ENVIRONMENTALLY SAFE OIL BASED DRILLING MUD USING ARTIFICIAL NEURAL NETWORK ALGORITHM

Due to increase in environmental legislation against the deposition of oil based mud on the environment, drilling companies have come up with an optimum drilling mud such as plant oil based mud with little or no aromatic content, which its waste is biodegradable. Optimum mud carry out the same function as diesel oil based drilling fluid and equally meets up with the HSE (Health, safety and environment) standard. It is expedient to determine the down hole mud properties such density in the laboratory or use of available correlation but most time; the range of data is not either reliable or unavailable. In this study, artificial neural network (ANN) was used to address the unreliable laboratory data and unavailable correlation for environmentally friendly oil based drilling mud such as jatropha and canola oil. The new artificial neural network model was developed for predicting the down hole mud density of diesel, jatropha and canola oil based drilling mud using 30 data sets. 60% of the data were used for training the network, 20% for testing, and another 20% for validation. The test results revealed that the back propagation neural network model (BPNN) showed perfect agreement with the experimental results in term of average absolute relative error returne

Design of an Intensified Reactor for the Synthetic Natural Gas Production through Methanation in the Carbon Capture and Utilization Context

112 páginasThe idea of a sustainable future has led to the exclusion of fossil fuels from development policies and the inclusion of low-carbon alternatives instead. The strategy must be holistic, as proposed by the carbon capture and utilization technologies alongside renewable energies. An example is converting CO2 into value-added products, such as CH4 or Synthetic Natural Gas (SNG), using surplus power of renewable alternatives, in a low-carbon footprint process. The chemical route for the synthesis of SNG from CO2 and H2 is a catalytic reaction known as CO2 methanation or Sabatier reaction. The methanation is an example of CO2 capture and utilization technologies' industrial application within the so-called Power-to-Methane (PtM) context. In this scenario, fixed bed reactors have been the reaction technology employed by default. However, their deficiency in handling the heat released from the highly exothermic Sabatier reaction or responding to the process' intermittency appropriately has been demonstrated. These drawbacks have aroused scientific interest in developing reactors better adapted to the PtM context demands. One approach is by intensifying the methanation process to increase the mass- and energy-transfer and improve its transient response. In this project, the phenomenological hot spots formation in fixed bed reactors used for the methanation industrial process was investigated through a parametric sensitivity analysis, simulating the reactor start-up. On the other hand, it was proposed a CFD simulation-aided conceptual design of a wall-coated reactor for the SNG production using an intensification strategy. The design was based on a reactor formed by single-pass and heat-exchanger stacked-plates. The reacting channel dimensions were defined, including the catalytic layer thickness, fulfilling a minimum quality threshold given by the CO2 conversion (≥ 95%). The proposed design was also intended to maximize the volume of processed gas while meeting the quality requirement, resulting in a throughput per reaction channel of ~12 ml/min. Likewise, the plates manifold geometry and dimensions that best promoted a flow rate uniform distribution were established as a function of the number of reacting channels. Finally, a preliminary dynamic analysis of the operation start-up and shutdown was performed, establishing that the designed reactor does not present a hysteresis behaviour, an ideal condition for intermittent environments.La idea de un futuro sostenible ha conllevado a suprimir el uso de combustibles de origen fósil de los planes de desarrollo y por el contrario incluir alternativas con baja huella de carbono. La estrategia debe ser holística, como lo proponen las tecnologías de captura y utilización de CO2 junto con las energías renovables. Un ejemplo es la conversión del CO2 en productos con valor agregado, como el CH4 o Gas Natural Sintético (GNS), utilizando la energía sobrante de las alternativas renovables, en un proceso con baja huella de carbono. La ruta química para síntesis de GNS a partir de CO2 e H2 es una reacción catalítica que se conoce como metanación de CO2 o reacción de Sabatier. La metanación es un ejemplo de aplicación industrial de las tecnologías de captura y utilización de CO2 en lo que también se conoce como el contexto Power-to-Methane (PtM). En ese ámbito, los reactores de lecho fijo han sido la tecnología de reacción utilizada por defecto. Sin embargo, se ha demostrado su incapacidad para manejar el calor liberado producto de la reacción de Sabatier (altamente exotérmica), o de responder apropiadamente a la intermitencia del proceso. Estas dificultades han despertado el interés científico por desarrollar reactores que se adapten mejor a las exigencias del contexto PtM. Una propuesta yace en intensificar el proceso de metanación, incrementando la transferencia de masa y energía además de mejorar su respuesta transitoria. En este proyecto se estudió, por un lado, la formación fenomenológica de puntos calientes en reactores de lecho fijo utilizados industrialmente para el proceso de metanación a través de un análisis de sensibilidad paramétrico, simulando el arranque del reactor. Por el otro lado, se propuso un diseño conceptual asistido por simulación CFD de un reactor de pared recubierta para la producción de GNS a través de una estrategia de intensificación. El diseño partió de un reactor formado por platos apilados de intercambio de calor de un solo paso. Se definieron las dimensiones del canal de reacción, incluyendo el grosor de la capa catalítica, que cumplían con el umbral mínimo de calidad dado por la conversión de CO2 (≥ 95%). El diseño propuesto también tuvo por objeto maximizar el volumen de gas procesado, cumpliendo a la vez con el requisito de calidad, lo que resultó en un rendimiento por canal de reacción de ~12 ml/min. Así mismo se estableció la geometría y dimensiones del colector del plato que mejor favorecían una distribución uniforme de la velocidad del flujo en función del número de canales de reacción. Por último, se realizó un análisis dinámico preliminar del arranque y apagado de la operación, estableciendo que el reactor diseñado no presenta un comportamiento de histéresis, ideal para un entorno con alta intermitencia.Maestría en Diseño y Gestión de ProcesosMagíster en Diseño y Gestión de Proceso

Model Predictive Control of Gas Processing Plant Focused on Depropanizer Column

The objective of this project is to improve the energy efficiency and reduce the operation cost for gas processing plant focused on de-propanizer column by implemented the advance process control namely Model Predictive Control. In gas processing plant, 60% of energy used for chemical industries is from distillation processes. To improve the energy efficiency ofdistillation column for gas processing plant, model predictive control is one of technology introduced to the distillation process control system that will overcome this problem compare to conventional controller. In this project, a study 2x2 model predictive control which consist of two manipulate variable and two control variable for de-propanizer column of gas processing plant. By doing the model predictive controller implementation, plant model development which consists of steady state and dynamic model is required by using HYSYS simulation. Step test is necessary which will then calculate the transfer function by using MATLAB system identification for model predictive control design and implementation. And lastly, Comparison between model predictive control and a conventional controller is desired which shown that model predictive controller has better performance and small energy consumption compare to conventional controller