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The effective thermal conductivity of packed beds of magnesium-manganese oxide pellets is a crucial parameter for engineering Magnesium Manganese Oxide (Mg-Mn-O) thermochemical energy storage devices. We have measured the effective thermal conductivity of a packed bed of 3.66 ±0.516 mm sized magnesium manganese oxide (Mn to Mg molar ratio of 1:1) pellets in the temperature range of 300 to 1400°C. Since the material is electrically conductive at temperatures above 600°C, the sheathed transient hot wire method is used for measurements. Raw data is analyzed using the Blackwell solution to extract the bed thermal conductivity. The effective thermal conductivity standard deviation is less than 10% for a minimum of three repeat measurements at each temperature. Experimental results show an increase in the effective thermal conductivity with temperature from 0.50 W/m °C around 300°C to 1.81 W/m °C close to 1400°C. We propose a dual porosity model to express the effective thermal conductivity as a function of temperature. This model also considers the effect of radiation within the bed, as this is the dominant heat transfer mode at high temperatures. The proposed model accounts for micro-scale pellet porosity, macro-scale bed porosity, pellet size, solid thermal conductivity (phonon transport), and radiation (photon transport). The coefficient of determination between the proposed model and the experimental results is greater than 0.90.

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This paper presents a model predictive controller (MPC) to regulate temperature inside a tubular solar reactor for production of solid-state solar fuel as a long-term thermal storage. The reactor tube is surrounded with heating elements over a portion of its length defined as the heating zone where the reaction occurs. Inert particles enter the reactor from its top-end and they flow downward due to gravity. Simultaneously, a counter-current flowing gas is supplied from the lower end of the reactor to recuperate the sensible heat and mix with the inert particles. The solar fuel is produced via a thermochemical reaction that occurs at a temperature range between 1000°C and 1500°C. This system is analyzed numerically to formulate a low-order physical model considering heat transfer modes and the reaction kinetics. The numerical model is implemented to design a model-based MPC controller where the measured temperature inside the heating zone is fed back to the control system and the control system produces control actions fed to the reactor in a closed-loop. The control system consists of a MPC code linked to an adaptive system identification code that updates system parameters online to ensure system robustness against external disturbances, model mismatches, and uncertainty. The MPC controller parameters are tuned to optimize the system performance with minimum steady-state error and overshoot. The controller is tested to track different temperature ranges between 1000°C and 1500°C with different particles/gas mass flow rates and ramping temperature profiles. Results show that the MPC controller was successful in regulating the reactor temperature within ± 5°C of its setpoint and maintaining robust performance with minimum input effort when subjected to external disturbances.