The chemical interactions that occur between NO, SO2, and ClO2 are investigated. In focus is the oxidation of NO with gaseous ClO2 for simultaneous removal of NO x and SO x from combustion-derived flue gases. Laboratory-scale experiments were conducted to examine the conversion of NO to NO2, under the following conditions: temperature range of 100–180 °C; H2O concentrations in the range of 0%–25%; ClO2–NO molar ratios in the range of 0.2–0.6; and NO and SO2 flue gas concentrations in the ranges of 0–250 ppm and 0–1000 ppm, respectively. The results show that NO is oxidized efficiently by ClO2, whereas the ClO2–SO2 reactions are insignificant. The water concentration had no effect on oxidation, and the temperature had a limited effect, within the investigated ranges. The outcomes favor the process economics of the studied application, since ClO2 consumption is negligible with respect to SO2 oxidation.

The coabsorption of SO x and NO x supported by the enhanced oxidation of NO by ClO2(g) from flue gases is investigated through technical-scale experiments and simulations. In focus is NO x absorption and the oxidation of S­(IV) to S­(VI). The concept was tested using the flue gases from a 100 kW gas-fired furnace, to confirm findings previously obtained at the technical scale. The chemical interactions that occurred between NO x , SO x , and ClO2, used as an oxidizing agent, were investigated. The measurements confirm that the absorption of SO2 in the scrubber is efficient and mainly determined by saturation in the scrubber liquid. The absorption of NO2 peaked at 82%, when Na2SO3 and Na2CO3 were added to the scrubber liquid. The results indicate that the efficiencies of removal of NO x and SO x can be high (>90% and >99%, respectively). This work provides a first proof-of-concept on a technical scale and validates the reaction mechanism proposed in previous studies.

The concept of coabsorption of NO2 and SO2 from flue gases, in combination with the enhanced oxidation of NO by ClO2(g), is studied on three scales, 0.2, 100, and 400 N m3/h, all with flue gases of different origins. The results obtained from each setup are presented, together with modeling that was applied to assess the scale-up of the concept and to validate the model. The measurements confirm that ClO2 is highly selective toward NO oxidation for temperatures in the range of 70–155 °C. A comparison of the results obtained for each scale reveals that the 0.2 N m3/h setup confers a higher level of NO x absorption than the other setups, although the trends remain similar. Simulations of the results underpredict the level of NO2 absorption in the 0.2 N m3/h setup while capturing the levels of absorption in the 100 N m3/h setup. An important finding is the rapid and complete oxidation of S­(IV) in the presence of NO2, which is not represented in the reaction kinetics.

Co-absorption of NO2 and SO2 from flue gases, in combination with the enhanced oxidation of NO by ClO2(g), is studied for three different flue gas sources: a medium sized waste-to-heat plant; the kraft recovery boiler of a pulp and paper mill; and a cruise ship. Process modeling results are used to present the technical potential for each site together with cost estimation and optimization using a bottom-up approach. A process set-up is proposed for each site together with equipment sizing and resulting flows of process fluids. The simulation results, supported by experimental results, show that removal rates equal to or greater than current best available technologies are achievable with more than 90% of NOx and 99% of SO2 removed from the flue gas. The resulting cost of removing both NOx and SO2 from the flue gases is 2100 €/ton for the waste-to-heat plant, 800 €/ton for the cruise ship and 3900 €/ton for the recovery boiler. The cost estimation show that the consumption and cost of chemical additives will play a decisive role in the economic feasibility of the investigated concept, between 50% and 90% of the total cost per ton acid gas removed.

The aim of this paper is to evaluate the effect of the load forecasting errors to the operation costs of a gridconnected microgrid. To this end, a microgrid energy scheduling optimization model was tested with deterministic and stochastic formulations under two solution approaches i.e., day-ahead and rolling horizon optimization. In total, twelve simulation test cases were designed receiving as input the forecasts provided by one of the three implemented machine learning models: linear regression, artificial neural network with backpropagation, and long short-term memory. Simulation results of the weekly operation of a real residential building (HSB Living Lab) showed no significant differences among the costs of the test cases for a daily mean absolute percentage forecast error of about 12%. These results suggest that operators of similar microgrid systems could use simplifying approaches, such as day-ahead deterministic optimization, and forecasts of similar, non-negligible accuracy without substantially affecting the microgrid's total cost as compared to the ideal case of perfect forecast. Improving the accuracy would mainly reduce the microgrid's peak power cost as shown by its 20.2% increase in comparison to the ideal case.