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The influence of the briquetting process on SO2 and NO release characteristics, combustion properties and kinetic characteristics during biomass combustion was investigated. Two biomass (Wheat straw and Tree bulk) and two obtained briquettes were analysed. The briquetting process helps to prevent the release of SO2 and NO. The experimental results show that once the biomass is made into a briquette, when the reaction temperature is 900 ∘C, the sulphur release ratio for TB was reduced from 34.7% to 4.3% and for WS was reduced from 12.4% to 1.6%. When the reaction temperature increases to 1000 ∘C, the sulphur release ratio for TB was reduced from 73.4% to 30.4%, for WS it was reduced from 58.4% to 10.2%. SEM micrographs show that the compact structure of the TB-Briquette and WS-Briquette reduce the rate of SO2 and NO release during combustion. The thermogravimetry confirmed that the combustion performance of WS-Briquette is the best, while the TB-Briquette is the worst. According to the Coats-Redfern method, the fitting was performed at segments of 250 ∘C to 550 ∘C, and the correlation coefficient of the fitting degree was above 0.99. The effective collision rate of WS-Briquette is much higher than that of other briquettes. Compared to BR-1 and BR-2, trying to mix TB with WS to make a compound biomass briquette can enhance the combustion performance of TB-Briquette. The results may guide the upgrading of biomass briquettes technology and benefit the efficient application of biomass briquettes.

During the operation of supercritical CO $ _2 $ (sCO $ _2 $ ) turbomachineries, large pressure ratios, supersonic conditions and non-ideal gas fluid dynamic phenomenon may happen, which will decrease the whole cycle efficiencies. Hence non-standard designs for the turbomachineries geometry are needed. Successfully simulations in capturing non-ideal gas fluid dynamics and coupled with optimization algorithm are hard and currently not available in any commercial CFD software. Therefore, the problem of optimizing sCO $ _2 $ turbomachinery is difficult to solve, which has become a major obstacle to obtain compact and high-efficiency sCO $ _2 $ turbomachineries. In this study, a modularized geometry optimizer is developed to obtain the non-standard geometric designs for sCO $ _2 $ turbomachineries. Multiple techniques are applied to this optimizer, include the Nelder–Mead algorithm, Mahalanobis distance and stochastic algorithm. The newly developed optimizer can successfully find the optimum satisfying the objective function under given weighting factors. The computational cost can be reduced through a stochastic algorithm. To validate the optimizer, a convergent–divergent nozzle for air with a target Mach number equal to 2.4 is optimized. Different starting points and combinations of weighting factors are used to create a Pareto front. Adjust the weighting factors for different terms of the objective function leading the optimizer to go to different directions in n-dimensional spaces. Three optimized cases, one is Mach number optimized, one is outlet flow uniformity optimized and the other is compromised case, are picked out and analyzed. The results show that the optimizer can successfully find optimized geometry than the reference case and potentially save computational cost. Due to the modularized characteristics, the components of this optimizer can be replaced with any available techniques, which mean the optimizer can be applied to solve different types of optimization problems.