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.

This work presents an extension and validation for a control oriented model of cone crushers. Compared to earlier work, the aspect of energy consumption was added to the model. Validation was carried out using measurement data from two different crusher models and was based both on laboratory data and field experiment data. Using the results from the field trials, a plant simulator for a secondary crushing circuit has been implemented.

This study considers how to efficiently manage the thermal state of data servers by provisioning their cooling airflow dynamically. It introduces a thermal network framework that is tailored to capture the temperature dynamics of the on-board thermal inertiae. On top of this modeling, it devises LeakageCooling, a novel flow provisioning controller that balances the temperature dependent leakage dissipation within the electronic chips and the power consumed by the local fans to produce the cooling airflow. The prediction capabilities of the proposed modeling and the overall control performance are assessed over several relevant experimental scenarios on an Open Compute Windmill V2 server.