arXiv subjects

Michela Mulas

Publications and source records attributed to Michela Mulas.

5 recordsLinked to original sources

arXiv2025

Predictive control of wastewater treatment plants as energy-autonomous water resource recovery facilities

This work proposes an automatic control solution for the operation of conventional wastewater treatment plants (WWTPs) as energy-autonomous water resource recovery facilities. We first conceptualize a classification of the quality of treated water for three resource recovery applications (environmental, industrial, and agricultural water reuse). We then present an output-feedback model predictive controller (Output MPC) that operates a plant to produce water of specific quality class, while also producing sufficient biogas to ensure nonpositive energy costs. The controller is demonstrated in the long-term operation of a full-scale WWTP subjected to typical influent loads and periodically changing quality targets. Our results provide a proof-of-concept on the energy-autonomous operation of existing wastewater treatment infrastructure with control strategies that are general enough to accommodate a wide range of resource recovery objectives.

eess.SY↗

arXiv2025

A system-level approach to generalized feedback Nash equilibrium seeking in partially observed games

This work proposes an algorithm for seeking generalized feedback Nash equilibria (GFNE) in noncooperative dynamic games. The focus is on cyber-physical systems with dynamics which are linear, stochastic, potentially unstable, and partially observed. We employ System Level Synthesis (SLS) to reformulate the problem as the search for an equilibrium profile of closed-loop responses to noise, which can then be used to reconstruct a stabilizing output-feedback policy. Under this setup, we leverage monotone operator theory to design a GFNE-seeking algorithm capable to enforce closed-loop stability, operational constraints, and communication constraints onto the control policies. This algorithm is amenable to numerical implementation and we provide conditions for its convergence. We demonstrate our approach in a simulated experiment on the noncooperative stabilization of a decentralized power grid.

math.OC↗

arXiv2024

SLS-BRD: A system-level approach to seeking generalised feedback Nash equilibria

This work proposes a policy learning algorithm for seeking generalised feedback Nash equilibria (GFNE) in $N_P$-player noncooperative dynamic games. We consider linear-quadratic games with stochastic dynamics and design a best-response dynamics in which players update and broadcast a parametrisation of their state-feedback policies. Our approach leverages the System Level Synthesis (SLS) framework to formulate each player's update rule as the solution to a robust optimisation problem. Under certain conditions, rates of convergence to a feedback Nash equilibrium can be established. The algorithm is showcased in exemplary problems ranging from the decentralised control of unstable systems to competition in oligopolistic markets.

math.OC↗

arXiv2024

A model-based framework for controlling activated sludge plants

This work presents a general framework for the advanced control of a common class of activated sludge plants (ASPs). Based on a dynamic model of the process and plant sensors and actuators, we design and configure a highly customisable Output Model-Predictive Controller (Output MPC) for the flexible operation of ASPs as water resource recovery facilities. The controller consists of a i) Moving-Horizon Estimator for determining the state of the process, from plant measurements, and ii) a Model-Predictive Controller for determining the optimal actions to attain high-level operational goals. The Output MPC can be configured to satisfy the technological limits of the plant equipment, as well as operational desiderata defined by plant personnel. We consider exemplary problems and show that the framework is able to control ASPs for tasks of practical relevance, ranging from wastewater treatment subject to normative limits, to the production of an effluent with varying nitrogen content, and energy recovery.

eess.SY↗

arXiv2023

Benchmarking Domain Adaptation for Chemical Processes on the Tennessee Eastman Process

In system monitoring, automatic fault diagnosis seeks to infer the systems' state based on sensor readings, e.g., through machine learning models. In this context, it is of key importance that, based on historical data, these systems are able to generalize to incoming data. In parallel, many factors may induce changes in the data probability distribution, hindering the possibility of such models to generalize. In this sense, domain adaptation is an important framework for adapting models to different probability distributions. In this paper, we propose a new benchmark, based on the Tennessee Eastman Process of Downs and Vogel (1993), for benchmarking domain adaptation methods in the context of chemical processes. Besides describing the process, and its relevance for domain adaptation, we describe a series of data processing steps for reproducing our benchmark. We then test 11 domain adaptation strategies on this novel benchmark, showing that optimal transport-based techniques outperform other strategies.

cs.LG↗