A control-oriented Gaussian process regression (GPR) model of froth flotation is developed and compared to a previously developed parametric model. The model aims to predict the behaviour of froth flotation, taking into consideration which state variables are available from measurements: air recovery, top of froth bubble size, and pulp level. The framework encodes prior knowledge of a published flotation model. Each state is modelled using a separate GP, with a custom covariance function whose form is given by the flotation model. These kernels capture the interaction between the relevant state variables and manipulated variables. The model aims to balance the complexity required to explain such a complex process with the uncertainty of its instrumentation. To evaluate the ability of the GPR model to capture the process dynamics, the GP model is assessed using an industrial data set, demonstrating its capacity to improve the performance of state prediction. The purpose of the GPR model is to enable supervisory and advanced model-based control.

Human excreta contains most of the nutrients consumed in diets, making its recovery essential for sustainable sanitation. Separately collecting blackwater (faeces and urine) enables efficient nutrient and energy recovery. After anaerobic digestion, blackwater produces a nutrient-rich liquid that requires transportation and sanitation for use as fertiliser; concentrating this liquid reduces transport costs and environmental impact. This study investigates nutrient concentration in blackwater digestate using a pilot-scale evaporator designed to operate with waste heat as its primary energy source. Experiments were conducted under two pH conditions (6 and 2.8). The evaporator operated at 60 °C with enhanced heat transfer and gravity-based separation, achieving volume reduction factors of 87 (pH 6) and 85 (pH 2.8). pH strongly influenced nutrient solubility (phosphorus, magnesium, calcium) and equipment leaching. Pharmaceutical residues persisted in concentrates, indicating a need for additional treatment. Final concentrates contained NPK up to 6.7%, 1.5%, and 1.2%, respectively. The specific energy consumption (SEC) increased with concentration, ranging from 0.56 kWh to 1.1 kWh at 60 °C, approximately 1.4–2.8 times higher than the theoretical value. Equipment modifications for low pH and improved material selection could enhance efficiency and regulatory compliance. Future designs may also integrate heat recovery systems to reduce energy demand and improve sustainability.

This paper outlines the application of geometrically constrained extremum-seeking control (ESC) to optimize froth flotation circuits. Froth flotation is a complex process, presenting challenges for model-based optimization. Furthermore, the process depends on stable operations for performance, with PID control often utilized for this purpose. To address the problem of rigorous modeling and provide high adaptability to changing operational conditions, this paper suggests using geometrically constrained extremum-seeking control as a combined approach to reach optimal performance while stabilizing the process. For this purpose, we developed and performed simulations of a 4-cell flotation circuit. The simulations show how the controller can steer the four cells toward a stable (predefined) operation point and drive it toward an (unknown) optimum.

As our world becomes increasingly digitalized, data centers as operational bases for these technologies lead to a consequent increased release of excess heat into the surrounding environment. This paper studies the challenges and opportunities of industrial symbiosis between data centers’ excess heat and greenhouse farming, specifically utilizing the north of Sweden as a case study region. The region was selected in a bid to tackle the urgent urban issue of self-sufficiency in local food production. A synergetic approach towards engaging stakeholders from different sectors is presented through a mix of qualitative and quantitative methods to facilitate resilient data-center-enabled food production. The paper delivers on possible future solutions on implementing resource efficiency in subarctic regions.

We formulate a modeling and control framework aimed at direct liquid cooling of data servers. In our application scenario, the server's heat load is rejected into a liquid cooling circuit that extends to individual chips. We start with a comprehensive discussion of our modeling derivations. We then show how to dynamically provision the coolant, while 1) regulating the temperatures of any self-heating components within the safe operational envelope; 2) minimizing the coolant supply cost; and 3) increasing the server outflow temperature (a key performance objective toward heat recovery systems). We confirm experimentally the benefits of the proposed controlled cooling strategy over several realistic scenarios corresponding to different inlet coolant temperatures and computational loads.

We report some results from the application of a flipped classroom setup for a basic course in automatic control. The course setup has been developed with the specific intention of avoiding extrinsic motivators for the students.For a group of 34 students, gender, exam score, and attendance in classroom activities was recorded and a linear regression model for how exam score relates to attendance in classroom activities and gender was developed using Matlab. It is shown that student attendance in classroom activities that require a great deal of time-on-task and student-student interactions is closely related to exam score. In contrast, other classroom activities, and also gender, is shown to have no significant impact on exam score.

We study the economic operation of a free-cooling setup in which an Indirect Adiabatic Air Handler (IAAH) recovers heat from an array of server racks placed in a contained aisle. For this setting we propose two different control policies: in the first approach, the airflow supply rate to the racks is maintained constant while only the process- side operations of the IAAH are optimized. In the second approach, also the room-side rate is updated adaptively. Building on calibrated models of the IAAH and the servers, we design experimental trials considering different outdoor temperatures and humidity conditions as well as varying computational workloads. The in silico analysis contributes actionable insights on the optimal thermal and cost operations of the system.

This work considers how to provision efficiently the cooling airflow in server rooms. We focus on raised plenum and perforated tiles setups and devise ColdSpot: an optimizing thermal supervisor that adaptively regulates the cooling airflow across the floor plane. ColdSpot operates by pairing 1) model-free estimators of the supply flow requirements at each perforated tile and 2) a model-based cost optimizer that actuates the room's Air Conditioning Units (ACUs) to satisfy the above rate requirements. We propose to use an array of Gaussian Processes (GPs) to capture the nonlinear flow model of each tile and present a hierarchical control structure on top of this modeling. We validate in silico the prediction accuracy of GPs in this context and demonstrate the performance of ColdSpot in regulating the cold-isles temperatures in spite of varying heat and airflow transport disturbances within the room.