Maximizing Efficiency and Profitability in Power-to-X Projects through Model Predictive Controls: A Techno-Economic Perspective.

As the world grapples with the imperative to reduce reliance on fossil fuels and mitigate climate change, PtX technologies have emerged as a promising solution. PtX processes offer the ability to convert renewable energy into valuable synthetic fuels, with ammonia serving as a notable hydrogen carrier. However, the techno-economic optimization of PtX projects is crucial for ensuring the economic feasibility of such endeavors. This paper introduces the role of Model Predictive Controls (MPC) as a key solution to enhance the performance, operability, and economic viability of PtX projects, with a focus on the potential increase in annual production and associated benefits.

Drawbacks and Shortcomings of Instant Data-Based Control Strategies in Simulations

While traditional or passive control systems, relying on instant data without forecasts, have been foundational in the simulation of many renewable energy projects, their limitations become apparent when applied to the dynamic and complex simulations of PtX projects. In a simulated environment, instant data-based control strategies operate by setting the parameters and operational setpoints for the next timestep in response to the current instant data. However, these strategies lack the adaptability and foresight necessary for optimal performance in fluctuating renewable energy conditions, revealing challenges in maintaining efficiency, stability, and operability using control strategies dependent solely on instant data.

The constrained ability to anticipate and dynamically adjust to forecasted weather data leads to suboptimal renewable energy resource utilization, heightened energy waste, frequent outages and increased operational risks. Furthermore, control strategies based on instant data may grapple with addressing the inherent non-linearities and uncertainties in PtX simulations, thereby limiting the achievement of the desired level of techno-economic optimization.

PtX projects are confronted with operational limitations, encompassing flexibility constraints, prolonged start-up times, and challenging ramp rates. The necessity for operational flexibility is pivotal in accommodating variable renewable energy inputs and, potentially, adapting to flexible market demands. Traditional control systems fall short in making timely process adjustments, leading to suboptimal process optimization under varying conditions. This is precisely where MPC excel.

Introduction to MPC

One notable feature of MPC is its ability to accommodate constraints on inputs, outputs, and states, a crucial consideration given the limitations inherent to PtX projects. This contrasts with other multi-input multi-output (MIMO) techniques which lack explicit handling of constraints. MPC possesses an additional advantage in its ability to factor in future developments, leveraging an internal prediction model of the controlled system. This predictive capability contributes to enhanced performance by considering events likely to occur several steps ahead. Furthermore, MPC stands out for its capacity to optimize multiple objectives, including economic considerations, control efficiency, and operability.

The operational sequence of MPC involves solving an optimization problem (cost function) at each time step, producing a sequence of optimum setpoints for a user-defined time horizon - a concept termed open-loop control. The controller then implements only the setpoints for the following timestep from the solution, discarding the remainder. This iterative process, known as receding horizon control, involves shifting the time horizon forward after each time step. Current state information is acquired from the plant model, and the entire sequence is repeated indefinitely, emphasizing MPC's role as a feedback control mechanism.

Benefits of MPC in Power-to-X Projects

MPC's distinctive capability to integrate predictive models using historical datasets positions it as an ideal solution for overcoming operational limitations during the simulation of PtX projects. The adaptive nature of MPC ensures seamless navigation through operational challenges, ultimately maximizing the efficiency, operability, and economic viability of PtX processes.

MPC utilizes mathematical models of the PtX process, predicting how the system will evolve over time under various conditions. By continuously optimizing control parameters based on these predictions, MPC ensures that the PtX process operates at maximum efficiency within the operational constraints of each of the plant components.

In the pursuit of enhanced PtX project performance, MPC assumes a central role. It serves as a dynamic orchestrator, continuously adjusting control parameters within simulated environments to optimize renewable energy resource utilization and process efficiency.

Notably, in renewable ammonia plants, MPC dynamically refines operational setpoints for crucial components like the electrolysis plant and ammonia synthesis loop based on historical or synthetic weather datasets. This real-time adaptability not only maximizes output per unit of input but also elevates the economic viability of PtX projects, forming the bedrock for sustained success in the evolving renewable energy landscape.

Furthermore, the adaption of MPC into PtX projects transforms the operational paradigm, reducing downtime and extending the simulated operational lifespan. This optimization, achieved through dynamic adjustments of operational parameters within simulations, enhances PtX projects' operational resilience and results in substantial cost savings across the simulated lifecycle.

The prolonged simulated operational lifespan, coupled with minimized downtime, underscores MPC's pivotal role as an enabler for sustained success in the renewable energy landscape. It emphasizes simulation-based optimization, ensuring PtX projects thrive under diverse conditions, contributing to long-term economic feasibility and realizing the full potential of renewable energy solutions.

Moreover, MPC's adaptive nature facilitates real-time responses to fluctuations in renewable energy availability, ensuring system stability and reliability. Visual representations of dynamic responses under MPC offer clarity on how the control system navigates through fluctuations, maintaining optimal conditions for energy conversion in PtX processes.

The potential for a up to 10% increase in annual production highlights MPC's crucial role in enhancing PtX projects' resilience to fluctuations and, ultimately, maximizing profitability. Through comparative analyses, MPC proves its economic prowess by optimizing various aspects, including adjusting setpoints for electrolysis and the ammonia loop, achieving process stability, and maximizing production.

This dynamic optimization ensures that PtX projects operate at the zenith of efficiency and profitability, with MPC emerging as a driving force behind the economic viability of renewable energy solutions, contributing to the sustained success and profitability of PtX endeavors.

Techno-Economic Optimization of Haber–Bosch Green Ammonia Production Plants with MPC

The Haber–Bosch process, while pivotal for green ammonia production, is not without its operational challenges. These challenges include the need to adhere to specific ramp rates during start-up and shutdown, ensuring efficient operation at varying production levels, and maintaining stable conditions during minimum load requirements. Traditional control systems often encounter difficulties in swiftly adjusting to these operational constraints, leading to suboptimal operability efficiency, and economic viability.

Recognizing the intricacies of the Haber–Bosch process, the implementation of MPC becomes a strategic solution. MPC's capacity to incorporate predictive models using historical data provides a dynamic approach to addressing operational constraints. By continuously adjusting control parameters based on evolving system behavior and forecasted weather data, MPC enables the optimization of ramp rates, precise control during minimum load conditions, and efficient adaptation to varying production levels.

The application of MPC in the Haber–Bosch green ammonia production plant yielded significant operational benefits. During start-up and shutdown, MPC ensured adherence to specific ramp rates, minimizing stress on critical components, and optimizing energy consumption. Additionally, the adaptive nature of MPC allowed the plant to efficiently operate at varying production levels, maximizing resource utilization and process efficiency.

The challenges associated with minimum load requirements can effectively be mitigated through MPC, ensuring stable and controlled operation even under these constraints. This not only enhanced the plant's overall performance but also contributed to prolonged equipment lifespan and reduced maintenance costs. The optimized ramp rates, precise control during minimum load conditions, and efficient adaptation to varying production levels resulted in a notable increase in annual production, contributing to the plant's profitability.

Introduction to BrainWorx

BrainWorx, an exclusive software suite developed by NetZeroWorx , elevates the design of PtX plants by seamlessly integrating Digital Twin Technologies, Model Predictive Controls (MPC), and Multivariant Optimization Techniques. This powerful combination empowers BrainWorx to effectively optimize PtX plant designs.

The Digital Twin creates virtual replicas of critical components within the PtX plant, including wind turbines, Solar PV farms, Battery Energy Storage Systems, Electrolyzers Arrays, Hydrogen Compression and Storage, Ammonia Synthesis, Water Treatment, and Cooling systems. These virtual replicas faithfully simulate the behavior and characteristics of these components, enabling in-depth analysis and optimization of their performance within the overall plant system.

BrainWorx conducts simulations of diverse operational scenarios, allowing users to adjust parameters and assess the plant's performance under various conditions. Through these simulations, users gain insights into the plant's behavior, identify areas for improvement, and make informed decisions to optimize overall performance. This capability facilitates a comprehensive assessment of the plant's efficiency and effectiveness across a spectrum of operating conditions.

The MPC utilizes a dynamic model of the system to predict its future behavior, optimizing control actions accordingly. By leveraging this predictive model, BrainWorx anticipates the system's response to various inputs and disturbances, computing optimal control actions based on these predictions. This proactive control approach enhances the overall efficiency, stability, and responsiveness of the PtX plant. Continuously assessing the system's state, predicting future outcomes, and calculating optimal control inputs in real-time, the MPC algorithm ensures iterative optimization, contributing to the plant's enhanced performance.

Combining Digital Twin Technologies, Model Predictive Controls, and Multivariant Optimization Techniques, BrainWorx achieves comprehensive design optimization for PtX plants. This approach enables accurate PtX plant optimization, intelligent decision-making, and more efficient project development processes, leading to enhanced performance and reduced costs.

BrainWorx generates comprehensive reports that provide project sponsors with valuable insights at every stage of the project development process, including scoping, feasibility, pre-FEED, and FEED up to Financial Close. These reports offer detailed information and analysis, empowering project sponsors to make well-informed decisions based on reliable data. Project sponsors can assess the feasibility of the project, evaluate different design options, select suitable vendors, and effectively plan for the subsequent stages of development. This unbiased and independent data-driven approach ensures that decision-making is grounded on accurate and up-to-date information, ultimately enhancing the project's chances of success.

Contact us at info.brainworx@netzeroworx.com to learn more about BrainWorx and how your PtX projects could benefit from it.