The group of Process Systems Engineering (PSE) is working on classical topics such as planning and scheduling, supply chain optimization and mathematical optimization of chemical processes and related operational problems with a special attention to energy topics. Special fields of focus are:
Other topics of interest are waste supply chain management, application of mixed integer linear and non-linear programming methods, and extending the PSE methods to new applications to improve sustainability with simultaneous focus and profitability and operability.
Here we list activities that the group members are participating at, e.g. conference presentations
Iiro Harjunkoski will give a presentation on the topic "Intensified Electricity Market Participation of Processes Industries". More about the Annual Meeting here.
Iiro Harjunkoski will co-present the work "A More Accurate Spinning Reserve Model Incorporating Physical Ramp Rates and Limited Participation". More about the event here.
Augusto Magalhães will present the work "Sparse Spectral Methods for Approximating PDE Solutions in Particle Flow". More about the event here.
Iiro Harjunkoski will present the work "Electricity Bidding with Variable Loads". More information about the conference here.
Iiro Harjunkoski will give a presentation on "Optimized Electricity Bidding under Price Uncertainty". More information about the conference here.
We host a monthly seminar series on various topics in PSE. The seminars are open to the public, will take place monthly, and shall provide an opportunity for researchers, students, and practitioners to learn and discuss emerging topics from process systems engineering.
The entire Aalto community (and beyond!) is invited to subscribe to our mailing list, where we will send calendar invitations, announcements, and related materials: Mailing List.
We are also open for outside presenters so if you are interested please contact Otacilio.
Title: Nonlinear Model Predictive Control with an Infinite Horizon Approximation
Presenter: Prof. Lorenz T. Biegler (Carnegie Mellon University, USA)
Abstract:
Increasing performance and efficiency of nonlinear progamming (NLP) solvers has led to large-scale applications for nonlinear model predictive control (NMPC) and real-time optimization (RTO). Current NMPC strategies are formulated as finite predictive horizon NLPs. However, ensuring stability and robustness properties may pose formidable challenges with a fixed horizon, particularly in the context of nonlinear dynamic processes. Motivated by these issues, we introduce an alternate moving horizon approach where an infinite-horizon time transformation is applied as the final sampling time in the horizon.
The key feature of this approach lies in solving the proposed NMPC formulation as an extended boundary value problem, using orthogonal collocation on finite elements. Numerical stability is ensured through a dichotomy property for an infinite horizon optimal control problem, which pins down the unstable modes, extends beyond open-loop stable dynamic systems, and leads to both asymptotic and robust stability guarantees. The efficacy of the proposed NMPC formulation is demonstrated a case study, which validates the practical application and robustness of the developed approach on real-world problems. Moreover, the extension of this NMPC approach to dynamic RTO will be presented as well.
When/where: 4.12.2025 13:00 – 14:00, Otakaari 1, Lecture room A1 (A123)
Open to the public. No registration required.
Title: Two-Stage Stochastic Energy Scheduling for Multi-Energy Rural Microgrids Incorporating Irrigation Systems and Biomass Fermentation
Presenter: Li Zhengmao (Aalto University)
Abstract:
Rural regions often face energy challenges due to unstable renewable generation, limited grid access, and inefficient use of local biomass resources. This talk presents a novel framework for multi-energy rural microgrids (MERMs) that integrate electricity, heat, biomass, and irrigation systems. A detailed anaerobic biomass fermentation (ABF) model is developed to capture realistic biochemical conversion from polymer substrates to methane, while an irrigation model is formulated to represent the interaction between environmental and agricultural factors. To handle diverse uncertainties in renewable generation, load demands, and weather conditions, a two-stage stochastic optimization approach is proposed and solved efficiently via a Progressive Hedging (PH)-based decomposition algorithm. Simulation results demonstrate that the proposed model achieves up to 68.4% cost reduction and significantly improves system resilience and flexibility. The talk concludes with discussions on future directions, including large-scale rural microgrids and simplified linearized ABF modeling for real-time applications.
When/where: 27.11.2025 (14:00 - 15:00) / Vuorimiehentie 2 (Circular Raw Materials Hub), C104 Platinium
Open to the public. No registration required.
Title: Data assimilation for dynamic systems
Presenter: José Augusto F. Magalhães (Aalto University)
Abstract:
A data assimilation (DA) problem for a dynamic system refers to the construction of a set of equations given a differential description of the evolution of the system and a collection of observations related to quantities of interest. The quantity of interest is often the state variable of such a system denoted as signal, with inferences about the signal being obtained from indirect and noisy observations. DA theorists work within a rather mature field, typically analysing the governing differential equations as mathematical constructs inspired by real problems. DA practitioners have adopted a particular formulation of these equations to accommodate real-world observations, developing algorithms that turn their implementation into a feasible task across a wide range of computational settings. The first part of this talk provides an overview of the major developments in these two branches over the past 60-70 years of the field. The second part focuses on Monte Carlo applications in DA for dynamic systems, particularly a class of problems involving stochastic (and possibly high-dimensional) nonlinear models. For these problems, Monte Carlo methods offer significant advantages over conventional DA algorithms. We illustrate these ideas through a series of examples, presenting the problem and its solution in the context of bioreactors, reaction-diffusion systems, and atmospheric models.
When/where: 23.10.2025 (14:00 - 15:00) / Vuorimiehentie 2 (Circular Raw Materials Hub), C104 Platinium
Open to the public. No registration required.
Title:
Predictive control and feedback equilibrium-seeking for sustainable water resource recovery
Presenter:
Otacilio B. L. Neto (Aalto University)
Abstract:
In this presentation, we investigate automation solutions to transition wastewater treatment plants (WWTPs) into sustainable water resource recovery facilities (WRRFs), that is, plants that produce goods and energy using wastewater as a raw material. We investigate optimal control and game-theoretical methods (i) for repurposing the already existing infrastructure to these emerging objectives, and (ii) for promoting a wastewater-centered market through an interconnected network of WRRFs. Our findings contribute a step towards zero-waste water resource recovery infrastructures, with our study on networked systems also benefiting applications from different domains, such as smart grids and supply chain management.
When/where: 25.09.2025 (14:00 - 15:00) / Kemistintie 1, Room D311 (KE5)
Open to the public. No registration required.
Iiro Harjunkoski has been appointed as Full Professor position at the Department of Chemical and Metallurgical Engineering as of 1 September 2024. The field of the professorship is Process Systems Engineering.
Professor Iiro Harjunkoski has been appointed as Deputy Head of the Department of Chemical Engineering and Metallurgy for the period 1 September 2025 to 31 December 2026.
Advances in predictive control and feedback equilibrium-seeking with applications to autonomous water resource recovery (title of the thesis).
