Use of Multiscale Data-Driven Surrogate Models for Flowsheet Simulation of an Industrial Zeolite Production Process

Abstract

The production of catalysts such as zeolites is a complex multiscale and multi-step process. Various material properties, such as particle size or moisture content, as well as operating parameters—e.g., temperature or amount and composition of input material flows—significantly affect the outcome of each process step, and hence determine the properties of the final product. Therefore, the design and optimization of such processes is a complex task, which can be greatly facilitated with the help of numerical simulations. This contribution presents a modeling framework for the dynamic flowsheet simulation of a zeolite production sequence consisting of four stages: precipitation in a batch reactor; concentration and washing in a block of centrifuges; formation of droplets and drying in a spray dryer; and burning organic residues in a chain of rotary kilns. Various techniques and methods were used to develop the applied models. For the synthesis in the reactor, a multistage strategy was used, comprising discrete element method simulations, data-driven surrogate modeling, and population balance modeling. The concentration and washing stage consisted of several multicompartment decanter centrifuges alternating with water mixers. The drying is described by a co–current spray dryer model developed by applying a two-dimensional population balance approach. For the rotary kilns, a multi-compartment model was used, which describes the gas–solid reaction in the counter–current solids and gas flows.

Elements of the Manufacture and Properties of Technical Catalysts

“…278 The amount of back-mixing and the total velocity of the particles in the axial direction depend on the filling, the speed of rotation, and possible internal structures within the furnace. 278 On the other hand, the heat transfer between the walls and the solids is dominated by convection at the solid−wall interface due to rotation. In addition, there is a convective heat transfer between the gas and solid and between the gas and the wall, and a radiation heat transfer between the gas−wall interface and the gas and solid surfaces.…”

“…The most common configuration of rotary kilns is counter current flow whereby the bed and gas flows are in opposite directions although cocurrent flow may be utilized in some instances, e.g., rotary driers, see Figure Figure presents the mass and heat fluxes between the different phases and regions within the rotary kiln . Mass transfer between the solid phase (the catalyst) and the gas phase occurs due to the evaporation of water and the combustion reaction. , In addition, there is a backward flow of solids due to possible back-mixing effects within the bed of particles .…”

“…Figure presents the mass and heat fluxes between the different phases and regions within the rotary kiln . Mass transfer between the solid phase (the catalyst) and the gas phase occurs due to the evaporation of water and the combustion reaction. , In addition, there is a backward flow of solids due to possible back-mixing effects within the bed of particles . The amount of back-mixing and the total velocity of the particles in the axial direction depend on the filling, the speed of rotation, and possible internal structures within the furnace .…”

Optimizing the Energy Consumption of the Porcelain Tile Manufacturing Process using Flowsheet Simulations

“…In recent decades, numerical simulations have become a valuable tool for optimizing and designing industrial equipment and evaluating operation parameters. Flowsheet calculations can be effectively applied for numerical investigations of complex plants, where different production steps are connected by material and energy streams [17][18][19].…”

„Grüne” Partikeltechnik?

“…B. die komplexe Aufarbeitung des Hamburger Hafenschlamms beschreiben 20. Diese grundlegenden Arbeiten wurden unter der Leitung von Stefan Heinrich, Werthers Nachfolger an der TU Hamburg, in einem Schwerpunktprogamm der DFG (SPP 1679 – „Dynamische Modellierung vernetzter Feststoffprozesse”) in Richtung dynamischer Simulation partikeltechnischer Prozesse weiter ausgebaut 21, 22.…”