Optimized data exploration applied to the simulation of a chemical process

In complex simulation environments, certain parameter space regions may result in non-convergent or unphysical outcomes. All parameters can therefore be labeled with a binary class describing whether or not they lead to valid results. In general, it can be very difficult to determine feasible parameter regions, especially without previous knowledge. We propose a novel algorithm to explore such an unknown parameter space and improve its feasibility classification in an iterative way. Moreover, we include an additional optimization target in the algorithm to guide the exploration towards regions of interest and to improve the classification therein. In our method we make use of well-established concepts from the field of machine learning like kernel support vector machines and kernel ridge regression. From a comparison with a Kriging-based exploration approach based on recently published results we can show the advantages of our algorithm in a binary feasibility classification scenario with a discrete feasibility constraint violation. In this context, we also propose an improvement of the Kriging-based exploration approach. We apply our novel method to a fully realistic, industrially relevant chemical process simulation to demonstrate its practical usability and find a comparably good approximation of the data space topology from relatively few data points.Comment: 45 pages, 6 figure

Pilot plant experimental studies of post combustion CO2 capture by reactive absorption with MEA and new solvents

AbstractThe main challenge for the CO2 post combustion capture from power plant flue gases is the reduction of the energy requirement for solvent regeneration. The required reduction can only be achieved by application of new solvents. For the validation of new solvents in the absorption/desorption process, a pilot plant (column diameters 0.125 m, absorber packing height 4.2 m, flue gas flow 30–110 kg/h, CO2 partial pressure 35–135 mbar) was built in the EUproject CASTOR. To obtain a baseline for testing of new solvents, first systematic studies were carried out with MEA in that plant. All important process parameters, i.e. CO2 content in the flue gas, CO2 removal rate ΨCO2, fluid dynamic load, and solvent flow rate were varied. These studies allow detailed insight into the process, e.g., a quantification of the different contributions to the overall regeneration energy (namely: desorption enthalpy, stripping steam, heating up of solvent feed and condensate recycle) as a function of the chosen process parameters. A rate-based model of the process based on a detailed physico-chemical model was implemented in the process simulator CHEMASIM. It is shown that the model is able to predict the experimental results for MEA. Besides MEA, two new solvents were studied in the pilot plant. A direct comparison of different solvents in such pilot plant experiments is not trivial. The comparison of only a few operating points for the new solvents with seemingly corresponding results for MEA can lead to wrong conclusions, since for each solvent an optimisation of the operating conditions is necessary. Only systematical studies allow a meaningful comparison. The technique that was used in the present work for this purpose was measuring data sets at constant CO2 removal rate (by adjustment of the regeneration energy in the desorber) and systematically varying the solvent flow rate. A minimal energy requirement for the given removal rate is found from theses studies. Only the optima for different solvents should be compared. By this procedure, one solvent candidate was identified that shows an advantage compared to MEA

Education in Process Systems Engineering: Why it matters more than ever and how it can be structured

This position paper is an outcome of discussions that took place at the third FIPSE Symposium in Rhodes, Greece, between June 20–22, 2016 (http://fi-in-pse.org). The FIPSE objective is to discuss open research challenges in topics of Process Systems Engineering (PSE). Here, we discuss the societal and industrial context in which systems thinking and Process Systems Engineering provide indispensable skills and tools for generating innovative solutions to complex problems. We further highlight the present and future challenges that require systems approaches and tools to address not only ‘grand’ challenges but any complex socio-technical challenge. The current state of Process Systems Engineering (PSE) education in the area of chemical and biochemical engineering is considered. We discuss approaches and content at both the unit learning level and at the curriculum level that will enhance the graduates’ capabilities to meet the future challenges they will be facing. PSE principles are important in their own right, but importantly they provide significant opportunities to aid the integration of learning in the basic and engineering sciences across the whole curriculum. This fact is crucial in curriculum design and implementation, such that our graduates benefit to the maximum extent from their learning

Optimal Design of Laboratory and Pilot-plant Experiments using Multiobjective Optimization

Performing an experimental design prior to the collection of data is in most circumstances important to ensure efficiency. The focus of this work is the combination of model‐based and statistical approaches to optimal design of experiments. The knowledge encoded in the model is used to identify the most interesting range for the experiments via a Pareto optimization of the most important conflicting objectives. Analysis of the trade‐offs found is in itself useful to design an experimental plan. This can be complemented using a factorial design in the most interesting part of the Pareto frontier

Optimal design of laboratory and pilot-plant experiments using multiobjective optimization

Performing an experimental design prior to the collection of data is in most circumstances important to ensure efficiency. The focus of this work is the combination of model-based and statistical approaches to optimal design of experiments. The knowledge encoded in the model is used to identify the most interesting range for the experiments via a Pareto optimization of the most important conflicting objectives. Analysis of the trade-offs found is in itself useful to design an experimental plan. This can be complemented using a factorial design in the most interesting part of the Pareto frontier

Challenges in process optimization for new feedstocks and energy sources

Current and future challenges of optimization in the process industry are discussed. The gap between academic research and industrial workflow is analyzed. Moreover, issues arising from the shift from conventional fossil fuels as both feedstock and energy source to nonconventional feedstocks (shale gas, tar sands, CO2 and biomass) and penetration of intermittent renewable energy are discussed. This manuscript focuses mainly on offline model-based optimization of design and operation, including the generation and selection of promising process alternatives for new feedstocks in conceptual design, multi-objective optimization, the estimation of thermodynamic parameters of new intermediates and the optimization of process operation under the volatile availability of the new feedstocks and energy sources. Moreover, a number of opportunities and needs for research and development are identified, including the simultaneous optimization of feedstocks, processes and products and a production able to process a variety of feedstocks and to utilize energy when it is cheap

Parameter Estimation Strategies in Thermodynamics

Many thermodynamic models used in practice are at least partially empirical and thus require the determination of certain parameters using experimental data. However, due to the complexity of the models involved as well as the inhomogeneity of available data, a straightforward application of basic methods often does not yield a satisfactory result. This work compares three different strategies for the numerical solution of parameter estimation problems, including errors both in the input and in the output variables. Additionally, the new idea to apply multi-criteria optimization techniques to parameter estimation problems is presented. Finally, strategies for the estimation and propagation of the model errors are discussed

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

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