Sequential Modular Approach Research Articles

Case-study of a flowsheet simulation using deep-learning process models for multi-objective optimization of petrochemical production plants

This work demonstrates a case-study to create machine-learning based process models from process data, which are connected into an overall process flowsheet and provide a high level of numerical stability for further multi-objective optimization. The used models are black-box and gray-box models, which are further compared to the proven LP approach. The proposed methodology is demonstrated using real-world measurement data from a refinery, involving a distillation unit, a hydrotreater, a reformer and an ethylene plant. The developed unit models are connected into an overall process flowsheet, which is solved by a sequential-modular approach and optimized in view of maximizing production margin and minimizing CO2-emissions. This work points out that the combination of engineering knowledge with data-driven techniques enables the incorporation of indirect information for process units, e.g., the crude oil composition vector for downstream units, leveraging the prediction performance of the unit models, compared to models not involving this information.

Sequential lidar sensor system simulation: a modular approach for simulation-based safety validation of automated driving

Validating safety is an unsolved challenge before autonomous driving on public roads is possible. Since only the use of simulation-based test procedures can lead to an economically viable solution for safety validation, computationally efficient simulation models with validated fidelity are demanded. A central part of the overall simulation tool chain is the simulation of the perception components. In this work, a sequential modular approach for simulation of active perception sensor systems is presented on the example of lidar. It enables the required level of fidelity of synthetic object list data for safety validation using beforehand simulated point clouds. The elaborated framework around the sequential modules provides standardized interfaces packaging for co-simulation such as Open Simulation Interface (OSI) and Functional Mockup Interface (FMI), while providing a new level of modularity, testability, interchangeability, and distributability. The fidelity of the sequential approach is demonstrated on an everyday scenario at an intersection that is performed in reality at first and reproduced in simulation afterwards. The synthetic point cloud is generated by a sensor model with high fidelity and processed by a tracking model afterwards, which, therefore, outputs bounding boxes and trajectories that are close to reality.

PARTEC 2019 – Overcome the Challenges in Particle and Powder Technology

AbstractNo abstract.

A tale of two rivers: Integrated hydro-economic modeling for the evaluation of trading opportunities and return flow externalities in inter-basin agricultural water markets

Water markets are water management instruments that can support water reallocation from low to high value agricultural uses. However, unless explicit consideration is given to water uses not directly involved in the trading (e.g. environment), water markets may fail to promote sustainable growth. In inter-basin water markets, the reallocation of water withdrawal entitlements from the selling basin typically involves the reallocation of return flows (the surface and subsurface water that leaves the agricultural system following withdrawal of irrigation water) along with consumptive uses, which may impair environmental flows and affect water uses downstream in the selling basin. This paper couples, using a sequential modular approach, a Positive Multi-Attribute Utility Programming (PMAUP) tool that models the behavior of irrigators and the Soil and Water Assessment Tool (SWAT) eco-hydrologic model to: i) assess the economic and water reallocation potential of a return flow-neutral inter-basin water market; and ii) explore the trading-return flow tradeoffs that stem from relaxing the neutrality principle. Methods are applied to the case of agricultural water markets in the Tagus-Segura interconnected basins in Spain. Results show that enforcing a return flow-neutral water market can reduce returns from trade by up to EUR 1.63 per m3 of conserved streamflow.

Predictive two-phase modeling and improvement for industrial UnipolⓇ polypropylene process – Development and a case study

This work presents a two-phase model describing propylene catalytic polymerization for UnipolⓇ technology with a fluidized bed reactor (FBR) using Aspen PlusⓇ software and the sequential modular simulation approach. The hydrodynamic aspects of the FBR were incorporated to provide better realism in describing the performance of the FBR. A detailed kinetic multi-site model for polymerization reactions occurring inside the FBR was proposed. Industrial data from a UnipolⓇ plant during normal operation were used to calibrate the model parameters. Obtained data sets were tested and screened to confirm data validity for establishing the model and averting incorrect outcomes. The model was validated against different industrial data and then was used to investigate the influences of different process variables on the plant's performance. Finally, the model was used to improve the process illustrating a prospective for remarkable savings in energy consumption in the process while enhancing the plant throughput.

Process simulation of a dual fluidized bed chemical looping air separation with Mn-based oxygen carrier

Chemical looping air separation (CLAS), a simple and efficient oxygen production technology, can be efficiently integrated with the moderate or intense low-oxygen dilution (MILD) combustion and oxy-fuel combustion technologies. Mn-based oxygen carrier is considered to be a hopeful candidate for the CLAS, but its redox kinetics has rarely been reported. In this work, the most suitable kinetic mechanisms for reduction and oxidation reactions are determined by thermogravimetric analyses. By coupling the obtained redox reaction kinetics and gas-solid flow hydrodynamics, a one-dimensional model of the interconnected fast fluidized bed and bubbling fluidized bed is built using sequential modular approach for simulating the chemical and physical phenomena in the CLAS process. The effects of main operation parameters on the CLAS performance are investigated by sensitivity analyses. The mole fraction of generated oxygen is found increasing with the increments of reduction temperature and air flow rate. Moreover, the system specific power consumption (SPC) is decreased when the temperatures of the two fluidized beds are similar. SPC can be as low as 0.076 kWh/m3 at the oxidation and reduction temperatures of 770 °C, the air flow rate of 50 Nm3/h, and the CO2 flow rate of 23 Nm3/h, which is 18% of the energy requirement of conventional cryogenic air separation system.

Ontology-Based Model to Support Ubiquitous Healthcare Systems for COPD Patients

Over the past 30 years, information technology has gradually transformed the way health care is provisioned for patients. Chronic Obstructive Pulmonary Disease (COPD) is an incurable malady that threatens the lives of millions around the world. The huge amount of medical information in terms of complex interdependence between progression of health problems and various other factors makes the representation of data more challenging. This study investigated how formal semantic standards could be used for building an ontology knowledge repository to provide ubiquitous healthcare and medical recommendations for COPD patient to reduce preventable harm. The novel contribution of the suggested framework resides in the patient-centered monitoring approach, as we work to create dynamic adaptive protection services according to the current context of patient. This work executes a sequential modular approach consisting of patient, disease, location, devices, activities, environment and services to deliver personalized real-time medical care for COPD patients. The main benefits of this project are: (1) adhering to dynamic safe boundaries for the vital signs, which may vary depending on multiple factors; (2) assessing environmental risk factors; and (3) evaluating the patient’s daily activities through scheduled events to avoid potentially dangerous situations. This solution implements an interrelated set of ontologies with a logical base of Semantic Web Rule Language (SWRL) rules derived from the medical guidelines and expert pneumologists to handle all contextual situations.

Rationalizing Systems Analysis for the Evaluation of Adaptation Strategies in Complex Human‐Water Systems

AbstractWater resources management is a nontrivial process requiring a holistic understanding of the factors driving the dynamics of human‐water systems. Policy‐induced or autonomous behavioral changes in human systems may affect water and land management, which may affect water systems and feedback to human systems, further impacting water and land management. Currently, hydro‐economic models lack the ability to describe such dynamics either because they do not account for the multifactor/multioutput nature of these systems and/or are not designed to operate at a river basin scale. This paper presents a flexible and replicable methodological framework for integrating a microeconomic multifactor/multioutput Positive Multi‐Attribute Utility Programming (PMAUP) model with an eco‐hydrologic model, the Soil and Water Assessment Tool (SWAT). The connection between the models occurs in a sequential modular approach through a common spatial unit, the “hydrologic‐economic representative units” (HERUs), derived from the boundaries of decision‐making entities and hydrologic responsive units. The resulting SWAT‐PMAUP model aims to provide the means for exploring the dynamics between the behavior of socio‐economic agents and their connection with the water system through water and land management. The integrated model is illustrated by simulating the impacts of irrigation restriction policies on the Río Mundo subbasin in south‐eastern Spain. The results suggest that agents' adaptation strategies in response to the irrigation restrictions have broad economic impacts and subsequent consequences on surface and groundwater hydrology. We suggest that the integrated modeling framework can be a valuable tool to support decision‐making in water resources management across a wide range of scales.

Comparison of continuous homogenous azeotropic and pressure-swing distillation for a minimum azeotropic system ethyl acetate/n-hexane separation

Abstract Continuous homogenous azeotropic distillation (CHAD) and pressure-swing distillation (PSD) are explored to separate a minimum-boiling azeotropic system of ethyl acetate and n-hexane. The CHAD process with acetone as the entrainer and the PSD process with the pressures of 0.1 MPa and 0.6 MPa in two columns are designed and simulated by Aspen Plus. The operating conditions of the two processes are optimized via a sequential modular approach to obtain the minimum total annual cost (TAC). The computational results show that the partially heat integrated pressure-swing distillation (HIPSD) has reduced in the energy cost and TAC by 40.79% and 35.94%, respectively, than the conventional PSD, and has more greatly reduced the energy cost and TAC by 62.61% and 49.26% respectively compared with the CHAD process. The comparison of CHAD process and partially HIPSD process illustrates that the partially HIPSD has more advantages in averting the product pollution, energy saving, and economy.

Novel system for dynamic flowsheet simulation of solids processes

The dynamic flowsheet simulation of solids processes is an area of increasing interest in recent years. Compared to the well-established flowsheet modelling of liquid-gas systems, the modelling of granular materials requires different approaches, strategies and algorithms. Therefore the new dynamic flowsheet simulation framework Dyssol has been developed within the Priority Program SPP 1679 “Dynamic simulation of interconnected solids processes (DYNSIM-FP)” of the German Research Foundation (DFG).In this contribution the architecture of the novel simulation framework and computational methods employed in it are presented. The system is based on the sequential-modular approach supplemented with partitioning and tearing methods. Waveform relaxation method, as well as several convergence methods and data extrapolation algorithms have been implemented to improve system performance and to increase convergence rate. To perform a correct calculation of multidimensional distributed parameters an approach with transformation matrices is used in the Dyssol system. Simulation case studies calculated with new system have shown good stability, convergence rate and agreement of simulation results with test systems and experimental results.

AN ISM BAND CONVENTIONAL CPW RECTENNA FOR LOW POWER LEVELS

This paper presents the design and fabrication of a coplanar waveguide (CPW) rectenna using a sequential modular approach. The rectenna is printed on high permittivity, low-loss board ARLON AD1000 (r = 10.35 and tan δ = 0.0023 @ 10 GHz). The rectifier section is realized with a single reverse-biased schottky diode SMS-7630 in reverse topology for which a diode model is obtained at −20 dBm for frequencies F 0 = 2.45 GHz and 2F 0 = 4.9 GHz. The low-pass filter and the impedance matching are synthesized from passive CPW structures. Co-simulation technique is used to overcome CPW simulation limitations and to integrate the diode characteristics. The antenna consists of a circular slot loop antenna with stub matching such that its input impedance is close to 50 Ω. The goal of this work is to design a rectifier to simplify and speed up the fabrication process of a rectenna array. We reduced the number of processes to etch the rectifier on the board and minimized the number of lumped elements. At −20 dBm, simulation of the rectifier with an ideal impedance matching network shows rectification at 2.45 GHz with efficiency of 12.8%. The rectifier and rectenna show efficiency of approximately 10% at an operating frequency of 2.48 GHz.

Simulation of Vinyl Chloride Process by Steady State Sequential Modular Approach - A Teaching Tool

Simulation of Vinyl Chloride Process by Steady State Sequential Modular Approach - A Teaching Tool

A globally convergent method for finding all steady‐state solutions of distillation columns

SignificanceA globally convergent method is proposed that either returns all solutions to steady‐state models of distillation columns or proves their infeasibility. Initial estimates are not required. The method requires a specific but fairly general block‐sparsity pattern; in return, the computational efforts grow linearly with the number of stages in the column. The well‐known stage‐by‐stage (and the sequential modular) approach also reduces the task of solving high‐dimensional steady‐state models to that of solving a sequence of low‐dimensional ones. Unfortunately, these low‐dimensional systems are extremely sensitive to the initial estimates, so that solving them can be notoriously difficult or even impossible. The proposed algorithm overcomes these numerical difficulties by a new reparameterization technique. The successful solution of a numerically challenging reactive distillation column with seven steady‐states shows the robustness of the method. No published software known to the authors could compute all solutions to this difficult model without expert tuning. © 2013 American Institute of Chemical Engineers AIChE J 60: 410–414, 2014

Model and Algorithm for Thermal Performance Calculation of Power Station Boiler Based on Process Steady-State Simulation Theory

Thermal performance calculation is the core task of designing power station boiler. By abstracting generalized components and generalized fluid nodes, and defining the process unit and process section at the logic level, the universal physical model of boiler was built in a particular form of flowsheet. Meanwhile, a sequential modular approach was proposed as the main algorithm for boiler thermal calculation based on process system steady-state simulation theory. Two key problems in the algorithm, i.e., module calculations and the logics of calling the modules calculations were explained. Finally, a practically developed system BESS, which has excellent flexibility and extensibility was presented. It turns out that the model and algorithm can be successfully employed in developing the general-purpose software for boiler thermal calculation.

Stochastic solution of population balance equations for reactor networks

This work presents a sequential modular approach to solve a generic network of reactors with a population balance model using a stochastic numerical method. Full-coupling to the gas-phase is achieved through operator-splitting. The convergence of the stochastic particle algorithm in test networks is evaluated as a function of network size, recycle fraction and numerical parameters. These test cases are used to identify methods through which systematic and statistical error may be reduced, including by use of stochastic weighted algorithms. The optimal algorithm was subsequently used to solve a one-dimensional example of silicon nanoparticle synthesis using a multivariate particle model. This example demonstrated the power of stochastic methods in resolving particle structure by investigating the transient and spatial evolution of primary polydispersity, degree of sintering and TEM-style images.

Modelling of an autoclave used for high pressure sulphuric acid/oxygen leaching of first stage leach residue. Part 1: Model development

Pressure leaching of the first stage leach residue in Base Metal Refinery (BMR) circuits aims to achieve high base metal dissolution with minimal precious metal leaching. Optimum autoclave operation is challenging because of the complex leaching chemistry, varying mineralogy of the feed material, and interaction between the different process variables. This research involved the modelling of the pressure leaching stages with flash recycle cooling at a typical BMR. The steady state solution employed the sequential modular approach in MATLAB, while the dynamic simulation involved the simultaneous solution of a set of differential equations, derived from mass and energy balances, in MATLAB. Part I of this communication presents a discussion of the Western Platinum Ltd. BMR process, an overview of relevant literature, and the strategies followed to develop both a steady state model and a dynamic model. Part II of this communication discusses the application of the models.

Effect of Step-Change Radiation Flux on Dynamic Characteristics in Tower Solar Cavity Receiver

The heat flux on the inner surface of the tower solar thermal power plant system will show the characteristics of noncontinuous step change, especially in nonnormal and transient weather conditions, which may result in a continuous dynamic variation of the characteristic parameters. Therefore, the research of dynamic characteristics plays a very important role in the operation and the control safely in solar cavity receiver system. In this paper, based on the noncontinuous step change of radiation flux, a non-linear dynamic model is constructed to obtain the effects of the non-continuous step change radiation flux and step change feed water flow on the receiver performance by sequential modular approach. The subject investigated in our study is a 1 MW solar power station constructed in Yanqing county, Beijing, China. This study has obtained the dynamic responses of the characteristic parameters in the cavity receiver such as drum pressure, drum water level, main steam flow, and main steam enthalpy under step change radiation flux. And the influence of step-change feed water flow to the dynamic characteristics has also been analyzed. The results could provide general guidance for security operation and control in solar cavity receiver system.

Sequential-Based Process Modeling of Natural Gas Combustion in a Fluidized Bed Reactor

A model for the simulation of a fluidized bed combustion reactor was developed using sequential modular approach in which two submodels for describing physical and chemical phenomena were integrated simultaneously. The adopted submodels were hydrodynamic and kinetic submodels, respectively. Dynamic two-phase model was used as the hydrodynamic submodel, and the latter submodel was extracted from literatures. The combustion reactor was divided into two regions, namely dense bed and freeboard. The dense bed was divided into several sections, determined by a newly introduced dimensionless number, in which gaseous phase was considered as a plug flow through the bubble phase, and a mixed flow through the emulsion phase. Moreover, energy balance and the effect of bubble growth were taken into account, and a new correlation for obtaining the critical temperature of natural gas combustion was proposed. Various sets of experimental data were derived from the literature and used to validate the proposed model, and a...

Sequential modular simulation of ethanol production in a three-phase fluidized bed bioreactor

A state-of-the-art sequential modular approach towards the modeling of a complex three-phase fluidized bed bioreactor has been introduced. The aim was to simulate the fermentation process of glucose for ethanol production using immobilized yeast in a gas–liquid–solid three-phase bioreactor. According to the newly-introduced dimensionless number (ASh number), a fluidized bed bioreactor was divided into several sections in which the three phases of emulsion, wake, and bubble were modeled parallel to each other. In the proposed model, two sub-models, namely, hydrodynamic and chemical reaction sub-models, were integrated to take the governing physical and chemical phenomena into account. Emulsion, wake, and bubble phases were considered as CSTR (continuous stirred-tank reactor), PFR (plug flow reactor), and bypass flow, respectively. Afterwards, mass transfer was taken into account right at the outlet of each section. The simulation results were compared with the experimental data derived from the literature in a wide range of gas velocity, liquid flow rate, biocatalyst particle size, and the concentration of glucose in the feed stream, which showed a great consistency. The simulation approach proposed in this study proved to be applicable in predicting the behavior of industrial three-phase fluidized bed reactors successfully.

A GENETIC ALGORITHMS BASED MULTI-OBJECTIVE OPTIMIZATION APPROACH APPLIED TO A HYDROMETALLURGICAL CIRCUIT FOR OCEAN NODULES

Several hydrometallurgical processes have been studied for the extraction of metals from lean ores utilizing various flow sheet options. Of particular significance is the grade of the ore being treated, the energy consumed and associated costs, options for byproduct recovery, and the relative price of the products. A process scheme needs to be optimized for simultaneously maximizing metal throughput and minimizing the direct operating costs incurred within constraints set for the operating variables. This leads to a multi-objective optimization problem. The range of input grades for raw material, which a flowsheet can handle, needs to be worked out based on an optimization exercise. A lean manganese-bearing resource such as polymetallic sea nodules has been chosen in this article for the development of an optimization approach based on which the input raw nodules grades are to be treated by a particular flowsheet. Only the chemical consumption costs have been adopted in this article as a measure of direct operating costs. A linear simulation model for the flowsheet has been developed, keeping a set of design parameters constant. The solutions generated by using a sequential modular approach become inputs to an optimization procedure based on a multi-objective genetic algorithm belonging to the differential evolution family. The variables considered in the optimization task are the grade of nodules and reactivity of different species inside the reactor. A nickel equivalent (t/h function) has been suggested as a measure of productivity, as it indirectly enhances the input manganese ore grade through a price ratio effect. This productivity function was maximized with the simultaneous minimization of direct chemical costs. Pareto optimal solutions were generated with grades of nodules and reactivity in the leach reactor as decision variables. The effect of the price ratio on the Pareto optimal solutions was also investigated. The various cases investigated clarifies the methodology for choosing an appropriate ore grade range for a given process flowsheet. Appropriate decisions regarding the nature of raw material to be used for a given flowsheet are then found on the basis of the Pareto optimal solutions.