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We provide a cyclone model for dynamical simulations in the pyro-process of cement production. The model is given as an index-1 differential-algebraic equation (DAE) model based on first engineering principle. Using a systematic approach, the model integrates cyclone geometry, thermo-physical aspects, stoichiometry and kinetics, mass and energy balances, and algebraic equations for volume and internal energy. The paper provides simulation results that fit expected dynamics. The cyclone model is part of an overall model for dynamical simulations of the pyro-process in a cement plant. This model can be used in the design of control and optimization systems to improve energy efficiency and reduce CO2 emission., Comment: 7 pages, 6 figures, 8 Tables

In this paper, we present a nonlinear model predictive control (NMPC) algorithm for systems modeled by semi-explicit stochastic differential-algebraic equations (DAEs) of index 1. The NMPC combines a continuous-discrete extended Kalman filter (CD-EKF) with an optimal control problem (OCP) for setpoint tracking. We discretize the OCP using direct multiple shooting. We apply an explicit singly diagonal implicit Runge-Kutta (ESDIRK) integration scheme to solve systems of DAEs, both for the one-step prediction in the CD-EKF and in each shooting interval of the discretized OCP. The ESDIRK methods use an iterated internal numerical differentiation approach for precise sensitivity computations. These sensitivities are used to provide accurate gradient information in the OCP and to efficiently integrate the covariance information in the CD-EKF. Subsequently, we present a simulation case study where we apply the NMPC to a simple alkaline electrolyzer stack model. We use the NMPC to track a time-varying setpoint for the stack temperature subject to input bound constraints.

This article explores the electrification in clay calcination, proposing a dynamic model and energy management strategy for the integration of electrified calcination plants into sustainable power grids. A theoretical dynamic modeling of the electrified calcination process is introduced, aiming at outlining temperature profiles and energy usage - thus exploring the feasibility of electrification. The model serves as a tool for optimizing parameters, estimating system behavior, and enabling model-based process control. An innovative energy management model is also presented, ensuring efficient assimilation of electrified calcination plants into the power grid. It encapsulates demand-supply balancing and optimizes renewable energy usage. In essence, we provide an insightful pathway to a more sustainable cement production, underlining the value of renewable energy sources and effective energy management in the context of clay calcination., Comment: Submitted to International Conference on Calcinated Clays for Sustainable Concrete 2024

We present a novel dynamic model of a flash clay calciner. The model consists of thermophysical properties, reaction kinetics and stoichiometry, transport, mass and energy balances, and algebraic constraints. This gives rise to a system of partial differential-algebraic equations (PDAE). Spatial discretization is performed to convert the PDAEs into a system of differential-algebraic equations (DAE). The model can be used, for example, to perform dynamic simulations with changing inputs, and process design and optimization. Moreover, it can be used to develop model-based control, which is relevant for flexible operation of a clay calcination plant for green cement production., Comment: Accepted for Advanced Control of Chemical Processes (ADCHEM) 2024, Toronto, Canada

We develop a mathematical model for dynamic simulation of an alkaline electrolyzer plant. The plant includes the stack, a water recirculation system and hydrogen storage with compressor. We model each component of the system with mass and energy balances. Our modeling strategy consists of a rigorous and systematic formulation using differential algebraic equations (DAE), along with a thermodynamic library that evaluates thermophysical properties. We perform a simulation with step power input. Dynamic modeling enables simulation and model-based optimization and control for optimal hydrogen production under varying operating conditions.

This paper presents models for renewable energy systems with storage, and considers its optimal operation. We model and simulate wind and solar power production using stochastic differential equations as well as storage of the produced power using batteries, thermal storage, and water electrolysis. We formulate an economic optimal control problem, with the scope of controlling the system in the most efficient way, while satisfying the power demand from the electric grid. Deploying multiple storage systems allows flexibility and higher reliability of the renewable energy system., Comment: Submitted to European Control Conference 2023