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.

Schedule an appointment

Selected customers

"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.

Book an appointment
Discover now

Use case examples


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

  • Heat is supplied at high temperature in the evaporator.
  • Heat is removed at a lower temperature in the condenser.

One way to reduce the energy requirement of the column is to raise the temperature of the waste heat generated in the condenser and use it in the evaporator. There are various technical solutions for this, such as using a heat pump. In this project, we compared this solution with steam stripping 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 producing synthesis gas—a mixture of hydrogen and carbon monoxide, which can be used to produce methane or methanol. The feedstocks are steam and CO2, which are split into H2, CO, and O2 using electrical power.

For further processing of the synthesis gas, an optimal ratio of H2 to CO is often desired. For example, the optimal 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 the composition of the feed gas. The latter will be examined more closely in this example.


In this example, a solid oxide electrolyzer is simulated to produce synthesis gas. At the start of the simulation, a gas mixture of steam and CO2 in a ratio of 1:1 is introduced. Later in the simulation, the proportion of steam in the feed mixture is slowly increased.


In the case of an equimolar feed composition, the H2/CO ratio is 1.2 at the present process parameters. Increasing the water content also increases the proportion of hydrogen in the synthesis gas. The optimal ratio of 3 for methanation is reached in this simulation at a water content of approximately 72%.


The speed of the methanation reaction is influenced by various factors, such as temperature, pressure, and the composition of the mass flow entering the reactor. In operation, these variables change dynamically for various reasons, thus affecting the performance of the methanation process. To reduce energy costs, it is often advisable to integrate the reactor into a complex energy system, for example, by utilizing the heat from the exothermic reaction in other subprocesses. For this purpose, it is important to understand how the reactor behaves at different temperatures to correctly design the system.


In this example, the pressure inside the reactor and the feed composition are kept constant. The temperatures of the reactor and the feed are simultaneously and continuously increased from 280°C to 350°C. This approach allows for the analysis of the reactor temperature's influence on methanation without interference effects.


As the reactor temperature increases, the heat flow generated by the reaction also increases. This can be attributed primarily to the increased reaction rate. Therefore, when designing a heat recovery system, it must be taken into account that more heat needs to be cooled at higher reactor temperatures!


The energy requirement in alkaline electrolysis is significantly influenced by the electrical voltage in the cells. The smaller the cell voltage at a given current density, the more efficient the electrolysis process is.

However, during operation, ohmic resistances—caused by the current flow through the cell components—lead to an increase in cell voltage. This results in power losses.

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


To illustrate the efficiency enhancement provided by the zero-gap arrangement, in this example, an electrolysis stack with conventional cells is simulated first and then with zero-gap cells. The applied current density is increased during the simulation, and the corresponding cell voltage is recorded. The result is the polarization curve - a relationship between the current density and the cell voltage.


As the current density increases, so does the voltage in the cells. However, compared to the conventional cell, the curve for the zero-gap alternative rises much more gradually. For electrolysis operations, this means that after setting the desired current density, a lower voltage is present in the cells. 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 (Proton Exchange Membrane) stack. This heat must be dissipated from the electrolyzer to enable efficient operation. For many stacks, a maximum temperature rise (∆Tmax) between the inlet and outlet reactant stream is specified by the manufacturer. Exceeding ∆Tmax reduces the lifespan of the stack and thus the economic efficiency of the system.

One way to control this temperature rise is by adjusting the incoming mass flow so that ∆Tmax is not exceeded. To minimize pump power, it is advisable to keep this mass flow as low as possible. Therefore, the optimal temperature rise is at ∆Tmax. An appropriate control strategy helps to dynamically maintain this value..


A 2 MW electrolyzer, coupled with a wind energy plant of the same size, is considered. Depending on the wind strength, the electrolyzer is utilized to varying degrees. The temperature rise is regulated to ∆Tmax as described above. Two different control strategies are simulated and compared:

  • Variant 1: Measurement of temperatures at the inlet and outlet of the stack, followed by PI (Proportional-Integral) control of the mass flow
  • Variant 2: Model Predictive Control (MPC) of the mass flow based on the incoming electrical current


The graph shows that a significant improvement in control quality can be achieved through model predictive control. The overshooting of the temperature is considerably lower than with the first variant, and a stable state is reached more quickly. This example was presented by TLK Energy and Neuman & 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.

Book appointment directly
Danke! Ihre Anfrage wurde verschickt!
Oh nein! Etwas ist schiefgelaufen beim Abschicken des Formulars. Sind alle Felder ausgefüllt?
yann pellny

Your contact person

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