Bernt Lie

A simulation study on the abatement of CO2 emissions by de‐absorption with monoethanolamine

Because of the adverse effect of CO2 from fossil fuel combustion on the earth’s ecosystems, the most cost‐effective method for CO2 capture is an important area of research. The predominant process for CO2 capture currently employed by industry is chemical absorption in amine solutions. A dynamic model for the de‐absorption process was developed with monoethanolamine (MEA) solution. Henry’s law was used for modelling the vapour phase equilibrium of the CO2, and fugacity ratios calculated by the Peng–Robinson equation of state (EOS) were used for H2O, MEA, N2 and O2. Chemical reactions between CO2 and MEA were included in the model along with the enhancement factor for chemical absorption. Liquid and vapour energy balances were developed to calculate the liquid and vapour temperature, respectively.

Dynamic modelling of the absorber of a post-combustion CO2 capture plant: Modelling and simulations

Abstract Modelling work related to carbon dioxide (CO 2 ) capture technologies is of great importance with respect to the design, control, and optimization of the capture process. Development of dynamic models as such is important since there is much information embedded with the dynamics of a plant which cannot be studied with steady state models. A model for the absorption column of a post-combustion CO 2 capture plant is developed following the rate based approach to represent heat and mass transfer. The Kent–Eisenberg model is used to compute the transfer and generation rates of the species. Sensitivity of the model for different physiochemical property correlations is analyzed. The predictions of the dynamic model for the capture plant start-up scenario and operation of the absorption column under varying operating conditions in the up-stream power plant and the down-stream stripping column are presented. Predictions of the transient behaviour of the developed absorber model appear realistic and comply with standard steady state models.

Inventory control of particulate processes

Abstract In this work we address the problem of designing model-based controllers for particulate processes described by population balance (PB) models. We focus on PB models that are solved by numerical discretization, for which many standard control methodologies are not suitable due to the high order of these models. We interpret discretized PB models as chemical reaction networks and suggest to combine inventory control with techniques of stability of chemical reaction networks to design the controller. Inventory control is based on the idea of manipulating process flows so that certain extensive variables defining the system, called inventories, follow their setpoints. The whole system is stabilized by controlling the dominant inventories. The discretized PB is exploited in all aspects of controller design, from determining the controlled inventories to the final implementation of the control law. The methodology is illustrated with an industrial leaching reactor, the Silgrain ® process. We show that the discretized PB model takes the form of a Feinberg–Horn–Jackson zero-deficiency network, allowing us to prove stabilization of the whole system. The performance of standard inventory control and robust inventory control are investigated by simulation, with satisfactory results even in the presence of modeling errors.

Using multi sensor data fusion for level estimation in a separator

Abstract A data driven model is developed to be used as a soft sensor to predict the liquid and interface levels in an oil/water separator. The methodology uses a set of absolute pressure sensors together with multi sensor data fusion for estimation of the levels. Experimental results are provided for model validation.

State estimation and model-based control of a pilot anaerobic digestion reactor

A state estimator and various model-based control systems have been designed for a real anaerobic digestion (AD) pilot reactor fed with dairy manure. The model used is a modified Hill model which is a relatively simple dynamical AD process model. The state estimator is an Unscented Kalman Filter (UKF) which uses only methane gas flow measurement to update its states. The model and the state estimates are used in different control systems. One of the control systems aims at controlling the methane gas flow to a setpoint. Simulations indicate that the setpoint tracking performance of a predictive control system is considerably better comparing with PI control, while disturbance compensation is not much better. Consequently, assuming the setpoint is constant, the PI controller competes well with the predictive controller. A successful application of predictive control of the real reactor is presented. Also, three different control systems aiming at retaining the reactor at an operating point where the volatile fatty acids (VFA) concentration has a maximum, safe value are designed. A simulation study indicates that the best control solution among the three alternatives is PI control based on feedback from estimated VFA.

COMPARISON OF STATE ESTIMATION TECHNIQUES, APPLIED TO A BIOLOGICAL WASTEWATER TREATMENT PROCESS

Abstract In this paper the problem of optimal state estimation in a biological wastewater treatment process (WWTP) is considered. The standard Kalman filter (KF) and its extensions: the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), are used to estimate the unmeasured state. The prediction of the state with the standard KF is poor due to the high nonlinearity of the biological WWTP. Thus, the nonlinear estimation approaches are focused, with a comparison between the EKF and the UKF. The simulation results show that the UKF provides slightly better state estimate than the EKF for both an observable process and an unobservable process.

Predictive control of an intermittently aerated activated sludge process

This paper presents model-based optimal control and predictive control of a biological wastewater treatment process with intermittent aeration. The objective of the control is to design an aeration strategy which minimizes the energy consumption induced by the aeration system, with adherence to the EU effluent standards and the operating constraints. The developed optimization problem is used with a receding horizon in nonlinear MPC based on the complete ASM3 model. The MPC aeration profile guarantees that the plant fulfills the effluent requirements at any time over long time periods. Significant energy saving is also obtained when comparing MPC to three traditional rule-based control strategies.

Using event location in finite-difference methods for phase-change problems

Event location has been used in the implementation of an existing finite-difference method for phase-change problems. The finite-difference method results in a model that changes structure every time the interface crosses a spatial grid line. In the traditional methods, either a fixed time step has been used, or the time step has been calculated by an iterative procedure as the time for the interface to move a single space increment. By using event location, the implementation stage is simplified, and the size of the time steps is automatically adapted to the interface dynamics.

Optimal design and operation of a UASB reactor for dairy cattle manure

Optimal design and operation of a UASB reactor at a dairy farm is determined.Brute force optimization is based on a dynamic AD model and temperature models.Solutions provided are maximum gas flow, minimum volume, and maximum power surplus.The optimal solutions are improved if the biomass retention time is increased. Optimal design and operation of a planned full-scale UASB reactor at a dairy farm are determined using optimization algorithms based on steady state simulations of a dynamic AD process model combined with models of the reactor temperature and heat exchanger temperatures based on energy balances. Available feedstock is 6m3/d dairy manure produced by the herd. Three alternative optimization problems are solved: Maximization of produced methane gas flow, minimization of reactor volume, and maximization of power surplus. Constraints of the optimization problems are an upper limit of the VFA concentration, and an upper limit of the feed rate corresponding to a normal animal waste production at the farm. The most proper optimization problem appears to be minimization of the reactor volume, assuming that the feed rate is fixed at its upper limit and that the VFA concentration is at its upper limit. The optimal result is a power surplus of 49.8MWh/y, a hydraulic retention time of 6.1d, and a reactor temperature of 35.9?C, assuming heat recovery with an heat exchanger, and perfect reactor heat transfer insulation. In general, the optimal solutions are improved if the ratio of the solids (biomass) retention time to the hydraulic retention time is increased.

Parameter identifiability analysis and model fitting of a biological wastewater model

Abstract A biological wastewater treatment plant situated at Duvbacken in Gavle, Sweden is described. A dynamic model based on the Activated Sludge Model No. 2d (ASM2d) is developed in the simulation language Modelica, and is exported from dymola (a Modelica environment) into Matlab. It is of interest to adjust the parameters in the model such that the developed model fits better to experimental data from the Duvbacken plant. In this paper, a systematic approach for parameter identifiability analysis is described. A standard least squares criterion is used in combination with a direct optimization method to calculate the improved parameter estimates, and the adjusted model is validated against experimental data from the industrial plant.