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This paper presents a method for the control design of five-level DCC converters based on mixed-integer optimization and machine learning. The resulting controller is computationally simple and can be easily implemented on low-resource control hardware using simple nested "if-else" statements. The optimization problem is recalled from previous work by modifying the cost function to further enhance the dynamic performance. Additionally, and in contrast to previous works, the online implementation accomplished in this paper allows the system to cover a wider range of operating points. For this, the optimization problem is solved offline for several operating conditions, and the results are gathered into a dataset to train classification and regression trees (CARTs), which are later used online. Due to the generalization capability of the CARTs, a more flexible and less resource-intensive implementation is achieved which is capable of operating at points outside the ones considered in the training dataset. The resulting control strategy is compared in simulation and experiments with several alternative approaches found in the literature. This approach can be extended to other power converter topologies, allowing the implementation of optimized modulations. • Modulation of five-level DCC as a mixed-integer optimization problem. • Achievement of wider range of operating points. • Classification and regression trees used for implementation of optimization problem. • Guidelines for robust implementation of such tress. • In-detail comparison with existing approaches both in simulation and experiments. [ABSTRACT FROM AUTHOR]

[Display omitted] • Small Ni 1−x Mn x Co 2 O 4 nanoparticles were synthesized by low-temperature gel combustion. • All the nanoparticles revealed strong catalytic performance in 4-nitrophenol reduction. • The catalytic performance of Ni 0.5 Mn 0.5 Co 2 O 4 nanoparticles was superior to NiCo 2 O 4 and MnCo 2 O 4 nanoparticles. • >90 % of 4-nitrophenol could be reduced in 12 min. • The anomalous catalytic behaviors of the nanostructures in repeated cycles were explained. Developing efficient catalysts is one of the challenging tasks for protecting our environment from contamination. Here we present the synthesis of Ni 1−x Mn x Co 2 O 4 , noble-metal free multimetallic oxide nanoparticles of 9.5–25.0 nm average sizes by a low-temperature gel combustion method and their utilization as catalysts for rapid reduction of 4-nitrophenol (4-NP) to aminophenol (AP). Scanning electron microscopy, transmission electron microscopy, X-ray photoelectron spectroscopy, Raman spectroscopy, and vibrating sample magnetometry were used for the characterization of the nanostructures. All the nanostructures revealed strong catalytic performance in the reduction of 4-NP, and the performance of Ni 0.5 Mn 0.5 Co 2 O 4 nanoparticles was superior (>90 % reduction in 12 min) to that of NiCo 2 O 4 and MnCo 2 O 4 nanoparticles. While the 4-NP reduction process followed a pseudo-first-order kinetics for all the catalysts, the reduction efficiency of the samples manifested a complex trend from cycle to cycle. The anomalous catalytic behavior of the nanostructures in 4-NP degradation has been explained. [ABSTRACT FROM AUTHOR]