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In this study, magnetic biochar nanocomposites (FeC–S) were prepared by employing ethylene glycol as a liquefaction agent of corn stalks and solvent for the solvothermal synthesis approach. The eff...

Currently, the manual method using hand-held infrared temperature measurement instruments for measuring temperatures on the external surfaces of ethylene cracking furnace tubes is highly subjective and is affected by a number of prominent issues, such as the high temperature working environments, which leads to low efficiency and poor measurement accuracy. Hence, an automatic temperature measurement system based on infrared light is designed and realized. In the system, a dual-phase drive synchronization method is proposed to rotate the thermodetector during horizontal movements, thus realizing automatic batch temperature measurements of the furnace tubes. Moreover, a temperature processing algorithm is developed to automatically identify furnace wall and tube surface temperatures, filter out abnormal temperatures and select only high-quality temperature measurements prior to calculating the final result. Real temperature measurement experiments demonstrated that the dual-phase drive temperature measurement system and temperature processing method are effective and efficient. Compared with the traditional manual way, temperatures obtained using the proposed system are more stable and accurate.

Cloud workflow requires task assignment and virtual resource provisioning to work together in a collaborative manner for adaptive scheduling, thus to balance the interests of both supply and demand in the cloud service under the Service Level Agreements. Focusing on collaborative adaptive scheduling of cloud workflow task and virtual resources problem, we introduce fine-grained cloud computing system model and design a novel collaborative adaptive scheduling mechanism based on reinforcement learning and multi-agent society. Our results demonstrate the efficiency of the proposed scheduling scheme.

Two novel magnetic biochar composites (FeC-H and FeC-P) were synthesized using corn stalks and ferrous sulfate through hydrothermal method and traditional pyrolysis, respectively. The samples were characterized by Fourier transform infrared spectroscopy (FTIR), X-ray powder diffraction (XRD), vibrating sample magnetometer, and particle size analyzer. Batch experiments were conducted to investigate the ability of those samples to absorb aqueous phosphate. FeC-H had lower surface area than FeC-P, but more hydrophilic functional groups were detected on the rough surface of FeC-H. The impregnated iron was present as Fe3O4 in the prepared magnetic biochar composites. The introduced Fe3O4 resulted in high performance of magnetic separation and also played the role as adsorption sites for phosphate. FeC-H and FeC-P demonstrated higher sorption capacity than bare Fe3O4 due to the highly dispersed and smaller crystalline sizes of Fe3O4 particles loaded in corn stalk derived-biochar support. FeC-H had the best performance, with Langmuir adsorption capacity as high as 5.04 mg/g for phosphate. These results indicate that the magnetic biochar composites prepared from corn stalks by hydrothermal method (FeC-H) have potential as a high-efficiency and cost-effective adsorbent for phosphorus removal from wastewater.

Nutrients were extracted from corn stalks, peanut shells, de-oiled peanut meal, chicken manure, and sewage sludge by a subcritical water (SCW) hydrolysis reaction. Compared with the other feedstock, the aqueous phases extracted from de-oiled peanut meal showed the highest water-soluble organic carbon, amino acid, total nitrogen, and phosphorus contents. The effects of solution pH, final hydrothermal temperature, and reaction time on nutrient extraction from de-oiled peanut meal were investigated. The analysis showed that alkaline reagents promoted liquefaction. The highest yield of the total primary nutrients (82.6%) was obtained with extraction reaction at 180 °C for 1.5 h using 0.1 mol/L KOH. The liquid fraction from this reaction was investigated for its potential use as a fertilizer with germination experiments. A higher germination index and root activity were obtained using the liquid extract with the appropriate dilution. These results indicated that subcritical water hydrothermal treatment is a viable way to recover nutrients from biomass wastes. In addition, de-oiled peanut meal is a suitable feedstock for the production of nutrient-rich liquid extract.

Disulfide-crosslinked hollow polyelectrolyte microcapsules composed of thiolated chitosan (CS-SH) and hyaluronic acid (HA-SH) were prepared by combining the layer-by-layer (LBL) technique and horseradish peroxidase (HRP)-mediated oxidative cross-linking reaction in mild conditions. FITC–dextran-doped CaCO3 microspheres were used as template core and removed after LBL depositing CS-SH and HA-SH on the surface. The disulfide-crosslinked (CS/HA) microcapsules were readily fabricated by HRP-mediated oxidative coupling of the thiol groups in CS/HA shell layer in the presence of HRP (10 units/mL) and Tyramine hydrochloride (Tyr, 35 mmol/L). The kinetics of enzymatic disulfide-crosslinking reaction was investigated through the real-time monitoring of the consumption of thiol groups by UV absorption spectra. It found that the formation of disulfide linkages by the enzymatic thiol oxidation reaction showed a gradual acceleration. The disulfide-crosslinked CS/HA hydrogel were rapidly formed in gelation time between...

In this paper, for the problem of long task scheduling time and unbalanced system load in the task scheduling of cloud workflow. To minimize the task scheduling time and optimize load balancing as the scheduling goal, a Markov decision process model conforming to the cloud workflow environment is established. Based on this, a multi-objective optimization cloud workflow scheduling algorithm based on reinforcement learning is proposed. The algorithm combines Q_Learning features, adding a function with a weighted fitness value function in the Q_Learning reward function so that it can apply multi-objective optimization. The set of scheduling schemes is a Pareto optimal solution set, which can select the optimal scheduling scheme according to the user’s preference. Compared with other methods, this algorithm can reduce the execution time and optimize the system load. And this paper uses the real cloud workflow data to carry out the simulation experiment, and carries on the experiment through the simulation platform WorkflowSim. The result proves the effectiveness of this algorithm.

Effective resource management can not only satisfy resource requirements of user applications, but also optimize system utilization so that more applications can be supported by the cloud. By designing an obedient real cloud computing environment model and analyzing the executing progress of workflow, we proposed a novel resource provisioning scheme based on the multi-agent and reinforcement learning in this paper. Multi-agent based cloud resource monitor mechanism includes two kinds of agent: perception-agent and resource-agent. They are employing detection and statistics cloud platform system runtime performance information and workflows execute progress information. Then, we designed a cloud resource provisioning algorithm based on reinforcement learning. The algorithm based on various information obtains by agents, make resources provisioning decision. Experiment results demonstrated the effectiveness of the proposed cloud resources provisioning scheme.

To the problem of scheduling multiple DAG workflow applications with multiple priorities submitted at different times in cloud computing environment, a novel workflow scheduling algorithm based on reinforcement learning is proposed in this paper. In the workflow scheduling scheme, the number of VMs in resources pool is defined as state space; the runtime of user task is defined as immediate reward, and then interactive with cloud computing environment to obtain the optimization policy. We use real cloud workflow to test the proposed scheme. Experiment results show the proposed scheme not only can solve the fairness of scheduling multiple DAGs with the same priority level submitted at different times, but also can ensure that the execution of the DAGs with higher priorities cannot be influenced by the DAGs with lower priorities. More importantly, the proposed scheme can reasonably schedule multiple DAGs with multiple priorities and improve utilization rate of resources better.

How to fairly schedule the multiple workflow with multiple priorities submitted at different times has become an increasing concern in workflow management system (WMS). To solve the problem, a novel workflow scheduling algorithm based on reinforcement learning is proposed in this study. In our scheme, we first define some basic concepts of reinforcement learning in cloud computing, such as state space, action space and immediate reward. Then single DAG and multiple DAG cloud workflow scheduling algorithm based on reinforcement learning are designed respectively. Reinforcement learning sets up a policy to maximise the cumulative rewards in the long-term through the repetition of trial-and-error interactions in cloud computing environment. Finally, we analyse algorithm performance by using queuing theory. We use real cloud workflow to test the proposed scheme. Our results, on the one hand, demonstrate the proposed scheme can reasonably schedule multiple DAGs with multiple priorities and improve utilisation rate of resources better and, on the other hand, show optimisation object function achieves fair workflow scheduling in cloud computing environment.