Influences of synthesis methods and modifier addition on the properties of Ni-based catalysts supported on reticulated ceramic foams

A method of synthesizing Ni-based catalysts supported on α-Al2O3-based foams was developed. The foams were impregnated with aqueous solutions of metal chlorides under an air atmosphere using an aerosol route. Separate procedures involved calcination to form oxides and drying to obtain chlorides on the foam surface. The synthesized samples were subsequently reduced with hydrogen. With respect to the Ni/Al2O3 catalysts, the chloride reduction route enabled the formation ofa Ni coating without agglomerates or cracks. Further research included catalyst modification by the addition of Pd, Cu, and Fe. The influences of the additives on the degree of reduction and on the low-temperature reduction effectiveness （533 and 633 K） were examined and compared for the catalysts obtained from oxides and chlorides. Greater degrees of reduction were achieved with chlorides, whereas Pd was the most effective modifier among those investigated. The reduction process was nearly complete at 533 K in the sample that contained 0.1wt% Pd. A lower reduction temperature was utilized, and the calcination step was avoided, which may enhance the economical and technological aspects of the developed catalyst production method.

Constrained Multi-Objective Optimization With Deep Reinforcement Learning Assisted Operator Selection

Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable attention.Various constrained multi-objective optimization evolutionary algorithms(CMOEAs)have been developed with the use of different algorithmic strategies,evolutionary operators,and constraint-handling techniques.The performance of CMOEAs may be heavily dependent on the operators used,however,it is usually difficult to select suitable operators for the problem at hand.Hence,improving operator selection is promising and necessary for CMOEAs.This work proposes an online operator selection framework assisted by Deep Reinforcement Learning.The dynamics of the population,including convergence,diversity,and feasibility,are regarded as the state;the candidate operators are considered as actions;and the improvement of the population state is treated as the reward.By using a Q-network to learn a policy to estimate the Q-values of all actions,the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic performance.The framework is embedded into four popular CMOEAs and assessed on 42 benchmark problems.The experimental results reveal that the proposed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.

Integrating Conjugate Gradients Into Evolutionary Algorithms for Large-Scale Continuous Multi-Objective Optimization

Large-scale multi-objective optimization problems(LSMOPs)pose challenges to existing optimizers since a set of well-converged and diverse solutions should be found in huge search spaces.While evolutionary algorithms are good at solving small-scale multi-objective optimization problems,they are criticized for low efficiency in converging to the optimums of LSMOPs.By contrast,mathematical programming methods offer fast convergence speed on large-scale single-objective optimization problems,but they have difficulties in finding diverse solutions for LSMOPs.Currently,how to integrate evolutionary algorithms with mathematical programming methods to solve LSMOPs remains unexplored.In this paper,a hybrid algorithm is tailored for LSMOPs by coupling differential evolution and a conjugate gradient method.On the one hand,conjugate gradients and differential evolution are used to update different decision variables of a set of solutions,where the former drives the solutions to quickly converge towards the Pareto front and the latter promotes the diversity of the solutions to cover the whole Pareto front.On the other hand,objective decomposition strategy of evolutionary multi-objective optimization is used to differentiate the conjugate gradients of solutions,and the line search strategy of mathematical programming is used to ensure the higher quality of each offspring than its parent.In comparison with state-of-the-art evolutionary algorithms,mathematical programming methods,and hybrid algorithms,the proposed algorithm exhibits better convergence and diversity performance on a variety of benchmark and real-world LSMOPs.

实验用熔炉中同步进行结晶测试

在先前的文献中描述了一种空气冷却的测试管,该测试管拥有一系列的插座和插头(在结晶过程中使用的转换插座),可以安装在实验室箱式熔炉中。该装置的用途是调节物质在坩埚槽中结晶过程的晶面和结晶速率。在本文中我们讨论一种改进型的内部冷却器——锯齿状的空气冷却管(结晶架)。这种改进型的冷却器可以简单地搭建和操作,可以延长和弯曲成为各种形状,可以用于坩埚和熔炉管中。该冷却器可以通过简单的调控对不同结晶参数和物质同步进行结晶测试,利用实验用熔炉就可以达到从一系列新型的具有层状晶体结构的化合物中得到晶体的目的。

Forest Biomass Availability Analysis and Large-Scale Supply Options

Finnish forest companies aim to produce biodiesel based on the Fischer-Tropsch process from forest residues. This study presents method to evaluate biomass availability and supply costs to the selected biorefinery site. Forest-owners’ willingness to sell, buyers’ market share, and regional competition were taken into account when biomass availability was evaluated. Supply logistics was based either on direct truck transportation deliveries from forest or on railway/waterway transportation via regional terminals. The large biomass need of a biorefinery demanded both of these supply structures, since the procurement area was larger than the traditional supply area used for CHP plants in Finland. The average supply cost was 17 €/MWh for an annual supply of 2 TWh of forest biomass. Truck transportation of chips made from logging residues covered 70% of the total volume, since direct forest chip deliveries from forest were the most competitive supply solution in terms of direct supply costs. The better supply security and lower vehicle capacity needs are issues that would favour also terminal logistics with other raw-material sources in practical operations. One finding was that the larger the biomass need, the less the variation in biomass availability and supply costs, since almost the whole country will serve as a potential supply area. Biomass import possibilities were not considered in this study.