### Professor Harvey Arellano-Garcia

### Biography

### Research interests

My research expertise includes the application of mathematical methods to optimise process design, control and operation as well as model-based experimental analysis in process and energy systems. My current research activities are concentrated on the development and application of systems engineering approaches for process intensification and integration including dynamic process simulation, model-based analysis and experimental verification, modelling and optimization of complex chemical and biological production systems, and energy conversion systems with large structural diversity and a high number of elements. Particular attention is paid to the holistic view of the involved processes phenomena, micro and macro processes, process design and the final experimental verification using miniplant technologies including improved control and monitoring.

### My publications

### Publications

identification. Special attention is given to the compliance with safety restrictions and operating

requirements during online experiments. For doing so, we propose the integration of a controller into the

system identification algorithm. To avoid numerical problems regarding ill-conditioned matrices an

algorithm for local parameter identifiability analysis is used. In order to demonstrate the benefits of our

approach, the proposed procedure is validated in a real case study. Additionally, a PI-controller is tuned for

the identified system. Moreover, the accuracy of the system parameters estimated by the proposed

strategy was compared with results for a conventional open-loop step response technique.

engineering fields. The micro-scale fluid dynamics are important for the optimal processing study since

they have a dominating influence on the overall performance of the reactor. In this article, 3-dimensional

Computational Fluid Dynamics (CFD) simulations are carried out to portray the flow characters in the

CSTR of the mini-plant of the Collaborative Research Center InPROMPT coordinated by the Technische

Universität Berlin. In InPROMPT, the reactor is used to perform a rhodium-catalyzed hydroformylation of

long chain alkenes. In this study, cases of a vessel with and without baffles are compared. The results

show the existence of a high speed velocity toroidal zone in different horizontal sections in the CSTR

without baffles, comparing to the common pervasive conception which is the assumption of a complete

uniformity of the fluid velocity magnitude in a CSTR at the final steady state. Moreover, under the rotation

speed of 400 rpm, the baffles in the CSTR can significantly prevent the formation of a surface vortex, for

the mean tangential velocity magnitude is dramatically decreased. On the other hand, the mean radial and

axial velocity is tremendously increased when the baffles are added. Therefore, the baffles in the tank lead

to a greater vertical fluid exchange in CSTR. Besides, in the specific domain of CSTR region, both the fluid

velocity direction and magnitude carry on a cyclical variation, when the baffles are inserted into the CSTR.

The developed NLP model was implemented in GAMS. The time needed for its solution is around 60s while using the CONOPT solver. The NLP model results were successfully applied to a Colombian refinery for 3 consecutive weeks. The model was able to find the best use of installed equipments in CDUs through the preparation of a crude oil charge quasi-constant quality without matter the time period of the optimization. In each week, optimal crude oil flow rates towards each CDU (like new scenarios implemented in the refinery) were evaluated in a refinery global simulator with all downstream refining schemes in order to calculate the Refinery Gross Margin (RGM). In each analyzed case, the obtained RGM for new crude oil feeds was however better than that case without optimization with a economic benefit of up to 0.043 US$/bl equivalent to US$ 3.870.000 per year. This shows the effectiveness of a CDU NLP model within short term planning in the petroleum industry.

This contribution presents a strategy to develop a technology platform with a validated process model containing the following steps: analysis of the economic potential, software selection, process analysis, steady-state detection, parameter identification, and process optimization. The presented strategy is applied to an industrial plant of BASF SE in Ludwigshafen, Germany. The application of this platform exposes great economic potential. Firstly, a significant cost reduction can be achieved by reusing existing models during the development phase. Secondly, with the help of the technology platform soft-sensors are created, bottlenecks identified, and an optimization of process operating strategies is undertaken.

In this contribution the implementation of a general method for generation of derivative information out of the documentation level is presented. Exploiting the possibility of code generation given by the web-based modeling environment MOSAIC (Kuntsche et al. 2011), derivative information of model equations is generated either by symbolic derivatives or by coupling the models with state of the art automatic differentiation (AD) tools. This offers the modeler different methods of getting exact derivative values and opens the possibility of integrating the assessment of derivative evaluations within the modeling and simulation workflow.

RESULTS: In this work, a fast and accurate model for the activated sludge process in a sequencing batch reactor is proposed. For this purpose, the ASM3 extended for two-step nitrification?denitrification, a 15-state variable model built for a general description of the ASP, is reduced to match the specific conditions of sequencing batch reactor systems with shortcut biological nitrogen removal to a nine-state model and then further to a six-state and five-state model under appropriate assumptions. The proposed model maintains the two-step nitrification?denitrification process feature of the original model and can thus describe the bypass of nitrate, showing increased tractability and lower computer costs. Different approaches for model reduction together with an exhaustive analysis of the extended ASM3 model as well as the process are discussed.

CONCLUSIONS: The resulting model with only five differential equations reduces the calculation time by up to one order of magnitude, while maintaining a high description accuracy, demonstrating the advantages of model reduction.

Because the efficiency of the original approach deteriorates with an increasing number of uncertain parameters, the probability integration has been extended recently to the exploitation of sparse grids. In this work, additional techniques for improving the efficiency of the variable mapping approach are presented. Firstly, the solution of a subproblem, the so called shooting task is analyzed in detail and enhanced through an idea called here result recycling. Secondly, possible extensions are presented which make use of second order derivative information. The new methods are verified by application to an industrially validated process model of a vacuum distillation column for the separation of multicomponent fatty acids.

In previous simulation and optimization studies, one-dimensional models have been used to describe the conventional PBMR. However, due to radial diffusion and thermal conduction those models are not accurate enough. In this work, a two-dimensional model for the CPBMR is presented. Radial diffusion and thermal conduction in the packed-bed as well as in the reactor shell are considered while axial dispersion is neglected. In accordance with experimental studies, Knudsen?s diffusivity theory is applied to describe the flux through the membrane. The model is discretized using a combination of Lagrangian and Hermite collocating polynomials on finite elements. The two-dimensional model contains second order derivatives for the radial coordinate. Hence, continuity of both the collocated variable and the first derivative across all finite elements are required in that direction. In this case, Hermite polynomials are advantageous because they allow for the afore-mentioned continuity while negating the necessity of additional equality constraints.

As an initial configuration, a length of 20 cm is assumed for the CPBMR with two separate heating/cooling segments of each 10 cm. The tube-side and shell-side diameters are set to 7 and 10 mm, respectively. Preliminary studies have shown that five radial and twelve axial finite elements are required to ensure a stable performance of all optimization studies for the given initial configuration. The resulting large-scale NLP contains more than 130,000 variables. Most fluid properties and transport parameters are implemented as functions of local temperatures and concentrations rather than average values. A brief study shows that their joint influence cannot be neglected. Using La2O3/CaO as a catalyst with kinetics provided by Stansch et al. (1997), an overestimation of more than 25 percentage points can be observed in the yield of C2 hydrocarbons in a one

In this work, we have explored a more ambitious integrated system by coupling the production of methane and carbon dioxide via coal gasification with the DRMOCM unit. Briefly, our process utilises coal to generate value-added methane and ethylene. In addition, CO2 management is achieved through CO2 methanation and dry methane reforming. Potential mass and energy integration between two systems is proposed as well as the optimum conditions for synthetic natural gas production. The upstream gasification process is modelled to determine the influence of temperature, pressure, and feed composition in the methane yield. The results suggest that the key variables are temperature and hydrogen concentration, as both parameters significantly affect the methane and CO2 levels in the linking stream. This study reports for the first time the linking stream between the two systems with a high methane concentration and the appropriate amount of CO2 for downstream processing.

in gas separation processes, hence, the need for an improved knowledge on the

hydrodynamics of this

ow. It is well established that the interface area of

the rivulets is determined by the balance between inertia and surface tension,

i.e. the Weber number, which in light of the results presented here, are not

the only parameters involved, but also the inclination of the plate has an effect

on the balance of forces which determines the amount of gas-liquid interface

area. The analysis of the interface area in rivulet

ow demands, therefore, a

more complete physical explanation for packing design purposes. In this work,

we analyse the combined effect of both the inertia and the inclination of the

plate in the interface area of liquid rivulets using CFD and the Volume-of-Fluid

interface tracking method. As a result, we propose the use of the Froude number

to provide a more complete physical explanation on the interface area formation

of gravity-driven liquid rivulets.

The multiplicity of physico-chemical and biological processes, where phosphorus is involved, makes their accurate prediction using current mathematical models in biotechnology quite a challenge. In this work, an antibiotic production model of Streptomyces coelicolor is chosen as a representative case study in which major difficulties arise in explaining the measured phosphate dynamics among some minor additional issues. Thus, the utilization of an advanced speciation model and a multiple mineral precipitation framework is proposed to improve phosphorus predictions. Furthermore, a kinetic approach describing intracellular polyphosphate accumulation and consumption has been developed and implemented. A heuristic re-estimation of selected parameters is carried out to improve overall model performance. The improved process model predicts phosphate dynamics (Root Mean Squared Error d52h: -90 %, Relative Average Deviation d52h: -96 %) very accurately in comparison to the original implementation, where biomass growth/decay was the only phosphorus source-sink. In addition, parameter re-estimation achieved an improved description of the available measurements for biomass, total ammonia, dissolved oxygen and actinorhodin concentrations.

This work contributes to the existing process knowledge of biotechnological systems in general and especially to antibiotic production with S. coelicolor, while emphasizing the (unavoidable) need of considering both physico-chemical and biological processes to accurately describe phosphorus dynamics.

This study will focus on the development of an integrated IoT-Distributed energy systems (DES) model for the efficient energy management of a microgrid under the integration of the intermittent renewable energy resources. In this work, we expand the definition of flexible options to include demand and supply together with design and operation strategies using internet of things (IoT). Our framework brings weather data and sensor information into a virtual energy plant optimisation model that connects supplier and consumer to optimise potential flexibility gaps arising from supply and demand mismatch. The problem is posed as a hybrid mixed-integer linear programming (MILP) optimisation model combining flexibility analysis and optimal synthesis for integrating energy supply and demand, where environmental information is added to each stage. Finally, we combine traditional mathematical programming approaches such as flexibility analysis and optimal network synthesis and within a single optimisation framework combining IoT and urban DES.

In this work, we propose a different approach to the same problem. Instead of relying on a sufficient concentration of salts in rain water, we propose to convert the mechanical energy delivered by drop impacts directly into electrical energy by supporting a thin-layer solar panel with an array of piezo crystals. The advantage of this setup is that the solar-to-electric performance of such a panel is not affected by the added piezoelectric support. However, only a fraction of the kinetic energy of the falling rain drops can be converted due to the energy dissipation within the material of the thin-layer panel. We have conducted detailed modelling of kinetic energy harvesting process from the drop impact and spreading to the dissipation of mechanical strain through the panel to the generation of piezoelectric potential. The results illustrate the viability of this concept, but they are still to be confirmed experimentally and require an economic feasibility analysis to be performed.