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Dynamic Process Simulation with Modelica -
Simulating PtX Processes Dynamically

Is your current process simulation software too inflexible? Our Modelica library for process engineering allows you to design and optimize new Power-to-X processes.

Our simulation experts support you with the expertise from over 250 customer projects.

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"The process engineering of the future is dynamic! The trends of decarbonization, electrification, and renewable energies require new processes, often in dynamic operation. To unlock their potential, process simulations based on flexible open software solutions like the Modelica library PSL are a decisive advantage."

Prof. Dr.-Ing. André Bardow

Energy and Process Systems Engineering, ETH Zurich

Our offer

Dynamic Simulation of PtX Processes – Software and Consulting in One Package.


Expertise from 250+ successful customer projects

A team of 20 simulation experts with several years of experience is at your side, bringing the expertise from many successful customer projects.

the team of tlk energy working in the office
Simulation software

Modelica library optimized for PtX processes

Our PSL (Process Systems Library) is a Modelica library for process engineering.

  • In-house developed core for dynamic two-phase flows (encrypted)
  • All other equations are open
  • Easily customizable and expandable
  • Base PSL open source available (coming soon)
Our Approach

No dependency on a software provider

We are not ASPEN. As a small specialized provider, we do not want or can offer an all-encompassing software solution. Instead, we believe in the interplay of specialized solutions. Therefore, we rely on:

  • Open standards (Modelica, FMI)
  • Well-documented APIs
  • Partnerships with universities and other companies

Your new opportunities in dynamic simulation of PtX processes

Open Code

The open code makes it easy for you to look at and understand the equations.

Modular structure

Thanks to the modular, object-oriented structure, you can flexibly reuse models.

Open interfaces

Combine our models with models from other libraries, such as our TIL Library.

Export possible

Export models as FMUs to compute them anywhere at no extra cost.


This is how we start collaborating with our clients.

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Use case examples


In a distillation column, heat is often added or removed at two points:

  • Heat is added at high temperature in the evaporator
  • In the condenser, heat is dissipated at a lower temperature

One way to reduce the energy requirement of the column is to raise the temperature of the waste heat generated in the condenser and utilize it in the evaporator. There are various technical solutions for this, such as the use of a heat pump. In this project, we compared this solution with vapour recompression and simulated various process designs. The results were published by TNO at the DA 2022 in Toulouse: Comparison of VCHP and MVR assisted distillation of MEG-water mixture via dynamic simulations; I. Tyraskis, M. Saric, A. Marina, Y. Pellny, P. Padberg, M.Gräber


Co-electrolysis is an electrochemical process for the production of synthesis gas - a mixture of hydrogen and carbon monoxide, which can be used to produce methane or methanol. Water vapour and CO2 are used as starting materials, which are split into H2, CO and O2 using electric current.

An optimum ratio of H2 to CO is often required for further processing of the synthesis gas. For example, the optimum H2/CO ratio for methanation is a value of 3.

In co-electrolysis, the H2/CO value is determined by process parameters such as temperature, current density, or educt gas composition. The latter will be analysed in more detail in this example.


In this example, a solid oxide electrolyser for the production of synthesis gas is simulated. At the beginning of the simulation, a gas mixture of water vapour and CO2 is introduced in a ratio of 1:1. Later in the simulation, the proportion of water vapour in the reactant mixture is slowly increased.


In the case of an equimolar reactant composition, the present process parameters result in an H2/CO ratio of 1.2. Increasing the water content also increases the proportion of hydrogen in the synthesis gas. The optimum ratio of 3 for methanation is achieved in this simulation with a water content of around 72 %.


The speed of the methanation reaction is influenced by various factors, such as temperature, pressure and composition of the mass flow entering the reactor.  During operation, these variables change dynamically for various reasons and thus influence the performance of the methanation.

In order to reduce energy costs, it often makes sense to integrate the reactor into a complex energy system and to utilize the heat from the exothermic reaction in other sub-processes. To do this, it is important to understand how the reactor behaves when the temperature changes to design the system correctly.


In this example, the pressure in the reactor and the feed composition are kept constant. The reactor and feed temperatures are continuously increased from 280 to 350 °C at the same time. This allows the influence of the reactor temperature on methanation to be analysed without disruptive effects.


As the reactor temperature rises, the heat flow generated by the reaction increases. This is mainly due to the increased reaction rate. When designing a heat recovery system, it must therefore be considered that more heat must be dissipated when the reactor temperature increases!


The energy requirement for alkaline electrolysis is significantly influenced by the electrical voltage in the cells. The lower the cell voltage at an applied current density, the more efficient the electrolysis process is.

During operation, however, ohmic resistances — triggered by the current flow through the cell components — cause the cell voltage to increase. This results in power losses.

One measure to reduce ohmic losses is the zero-gap arrangement. In this cell design, the two porous electrodes lie directly on the separator so that the electric current no longer has to flow through the two electrolyte channels:


To illustrate the increase in efficiency due to the zero-gap arrangement, an electrolysis stack with conventional and then with zero-gap cells is simulated in this example. The applied current density is increased during the simulation and the resulting cell voltage is recorded. The result is the polarization curve - a correlation between the current density and the cell voltage.


As the current density increases, the voltage in the cells also increases. In contrast to conventional cells the curve in the zero-gap alternative rises much flatter. For electrolysis operation, this means that a lower voltage is present in the cells once the desired current density has been set. Ultimately, the lower voltage in the zero-gap cells leads to a reduced energy requirement.


During water electrolysis waste heat is generated in the PEM stack. This heat must be dissipated to enable efficient operation of the electrolyser. For many stacks, the manufacturer also specifies a maximum temperature rise ∆Tmax between the incoming and outgoing reactant flow. If ∆Tmax is exceeded, the lifetime of the stack is reduced and therefore also the economic efficiency of the system.

One way to influence this temperature increase is to adjust the incoming mass flow so that ∆Tmax is not exceeded. However, to keep the pump output low, it makes sense to keep this mass flow as low as possible. The optimum temperature rise is therefore ∆Tmax. A suitable control strategy also helps to maintain this value dynamically.


A 2 MW electrolyser is considered, to which a wind turbine of the same size is coupled. Depending on the wind strength, the electrolyser is utilized to a greater or lesser extent and the temperature rise is controlled to ∆Tmax as described above. Two different control strategies are simulated and compared:

  • Variant 1: Measurement of the temperatures at the inlet and outlet of the stack, subsequent PI control of the mass flow rate
  • Variant 2: Model Predictive Control of the mass flow based on the incoming electrical current


The graph shows that a significant improvement in the control accuracy can be achieved through model predictive control. The temperature overshoot is significantly lower than with the 1st variant and a stable state is reached more quickly. This example was presented by TLK Energy and Neuman and Esser at the International Modelica Conference 2023.

yann pellny presenting at a modelica conference stage
Yann Pellny at the International Modelica Conference

Let's discuss your use case for dynamic PtX process simulation.

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Your contact person

Yann Pellny (Product Owner PSL at TLK Energy) is looking forward to discussions in German, English, or French.