Preparation of Oily Sludge-Derived Activated Carbon and Its Adsorption Performance for Tetracycline Hydrochloride.

Oily sludge-derived activated carbon was prepared using the potassium hydroxide (KOH) activation method using oily sludge as a raw material, and one-factor experiments determined the best conditions for preparing activated carbon. The activated carbon's morphological structure and surface chemical properties were analyzed by scanning different characterization tools, and the adsorption behavior of tetracycline hydrochloride was investigated. The results showed that the optimum conditions for preparing oily sludge-derived activated carbon were an activation temperature of 400 °C, activation time of 30 min, activator concentration of 1 mol/L, and impregnation ratio of 2 mL/g. After activation, the activated carbon had more pores and a more orderly crystal structure arrangement, the specific surface area was 2.07 times higher than that before activation, and the surface was rich in functional groups such as -HO, -C-O, -C=C, and -C-H, which increased the active sites of activated carbon. Physicochemical effects dominated the adsorption process. It belonged to the spontaneous heat absorption process under the quasi-secondary kinetic and Langmuir isothermal models. The maximum monolayer adsorption capacity of KOH-activated carbon was 205.1 mg·g-1.

Multi-objective optimization of hydrogen production system based on the combined supercritical cycle and gas turbine plant.

In this paper, considering the hydrogen production cycle coupled with the supercritical CO2 cycle, hydrogen is produced as a clean fuel along with power and heat production. Also, the world's need for clean energy doubles the solutions to achieve clean hydrogen energy. The investigated cycle has a supercritical CO2 cycle with a combustion chamber where enriched fuel is introduced. Combustion products produce work in the gas turbine, and further hydrogen separation is formed with the help of water gas shift reaction and hydrogen separation membrane. In the thermodynamic analysis, combustion chamber known as the most irreversible member of set, where the most exergy is lost. The energy and exergy efficiency for whole set are 64.82%, 52.46% respectively. Produced hydrogen mass flow rate calculated 46.8 kg/h. Also, multi-objective optimization based on genetic algorithm were done and the results were reported. All calculation and optimization method has been done in MATLAB software.

The Influence of Halide Ion Substitution on Energy Structure and Luminescence Efficiency in CeBr2I and CeBrI2 Crystals.

This study aims to determine the optimum composition of the CeBr1-xIx compound to achieve the maximum light output. It is based on calculations of the band energy structure of crystals, specifically taking into account the characteristics of the mutual location of local and band 5d states of the Ce3+ ions. The band energy structures for CeBr2I and CeBrI2 crystals were calculated using the projector augmented wave method. The valence band was found to be formed by the hybridized states of 4p Br and 5p I. The 4f states of Ce3+ are located in the energy forbidden band gap. The conduction band is formed by the localized 5d1 states, which are created by the interaction between the 5d states of Ce3+ and the 4f0 hole of the cerium ion. The higher-lying delocalized 5d2 states of Ce3+ correspond to the energy levels of the 5d states of Ce3+ in the field of the halide Cl0 (Br0) hole. The relative location of 5d1 and 5d2 bands determines the intensity of 5d-4f luminescence. The bottom of the conduction band is formed by localized 5d1 states in the CeBr2I crystal. The local character of the bottom of the conduction band in the CeBr2I crystal favors the formation of self-trapped Frenkel excitons. Transitions between the 5d1 and 4f states are responsible for 5d-4f exciton luminescence. In the CeBrI2 crystal, the conduction band is formed by mixing the localized 5d1 and delocalized 5d2 states, which leads to quenching the 5d-4f luminescence and a decrease in the light output despite the decrease in the forbidden band gap. CsBr2I is the optimum composition of the system to achieve the maximum light output.

Effect of Hydrothermal Factors on the Microhardness of Bulk-Fill and Nanohybrid Composites.

This study evaluates the effect of aging in artificial saliva and thermal shocks on the microhardness of the bulk-fill composite compared to the nanohybrid composite. Two commercial composites, Filtek Z550 (3M ESPE) (Z550) and Filtek Bulk-Fill (3M ESPE) (B-F), were tested. The samples were exposed to artificial saliva (AS) for one month (control group). Then, 50% of the samples from each composite were subjected to thermal cycling (temperature range: 5-55 °C, cycle time: 30 s, number of cycles: 10,000) and another 50% were put back into the laboratory incubator for another 25 months of aging in artificial saliva. The samples' microhardness was measured using the Knoop method after each stage of conditioning (after 1 month, after 10,000 thermocycles, after another 25 months of aging). The two composites in the control group differed considerably in hardness (HK = 89 for Z550, HK = 61 for B-F). After thermocycling, the microhardness decrease was for Z550 approximately 22-24% and for B-F approximately 12-15%. Hardness after 26 months of aging decreased for Z550 (approximately 3-5%) and B-F (15-17%). B-F had a significantly lower initial hardness than Z550, but it showed an approximately 10% lower relative reduction in hardness.

Individualized Short-Term Electric Load Forecasting Using Data-Driven Meta-Heuristic Method Based on LSTM Network.

Short-term load forecasting is viewed as one promising technology for demand prediction under the most critical inputs for the promising arrangement of power plant units. Thus, it is imperative to present new incentive methods to motivate such power system operations for electricity management. This paper proposes an approach for short-term electric load forecasting using long short-term memory networks and an improved sine cosine algorithm called MetaREC. First, using long short-term memory networks for a special kind of recurrent neural network, the dispatching commands have the characteristics of storing and transmitting both long-term and short-term memories. Next, four important parameters are determined using the sine cosine algorithm base on a logistic chaos operator and multilevel modulation factor to overcome the inaccuracy of long short-term memory networks prediction, in terms of the manual selection of parameter values. Moreover, the performance of the MetaREC method outperforms others with regard to convergence accuracy and convergence speed on a variety of test functions. Finally, our analysis is extended to the scenario of the MetaREC_long short-term memory with back propagation neural network, long short-term memory networks with default parameters, long short-term memory networks with the conventional sine-cosine algorithm, and long short-term memory networks with whale optimization for power load forecasting on a real electric load dataset. Simulation results demonstrate that the multiple forecasts with MetaREC_long short-term memory can effectively incentivize the high accuracy and stability for short-term power load forecasting.

Energy Structure and Luminescence of CeF3 Crystals.

The results of the calculation of the energy band structure and luminescent research of CeF3 crystals are presented. The existence of two 5d1 and 5d2 subbands of the conduction band genetically derived from 5d states of Ce3+ ions with different effective electron masses of 4.9 me and 0.9 me, respectively, is revealed. The large electron effective mass in the 5d1 subband facilitates the localization of electronic excitations forming the 4f-5d cerium Frenkel self-trapped excitons responsible for the CeF3 luminescence. The structure of the excitation spectra of the exciton luminescence peaked at 290 nm, and the defect luminescence at 340 nm confirms the aforementioned calculated features of the conduction band of CeF3 crystals. The peculiarities of the excitation spectra of the luminescence of CaF2:Ce crystals dependent on the cerium concentration are considered with respect to the phase formation possibility of CeF3.

A Software Deep Packet Inspection System for Network Traffic Analysis and Anomaly Detection.

In this paper, to solve the problem of detecting network anomalies, a method of forming a set of informative features formalizing the normal and anomalous behavior of the system on the basis of evaluating the Hurst (H) parameter of the network traffic has been proposed. Criteria to detect and prevent various types of network anomalies using the Three Sigma Rule and Hurst parameter have been defined. A rescaled range (RS) method to evaluate the Hurst parameter has been chosen. The practical value of the proposed method is conditioned by a set of the following factors: low time spent on calculations, short time required for monitoring, the possibility of self-training, as well as the possibility of observing a wide range of traffic types. For new DPI (Deep Packet Inspection) system implementation, algorithms for analyzing and captured traffic with protocol detection and determining statistical load parameters have been developed. In addition, algorithms that are responsible for flow regulation to ensure the QoS (Quality of Services) based on the conducted static analysis of flows and the proposed method of detection of anomalies using the parameter Hurst have been developed. We compared the proposed software DPI system with the existing SolarWinds Deep Packet Inspection for the possibility of network traffic anomaly detection and prevention. The created software components of the proposed DPI system increase the efficiency of using standard intrusion detection and prevention systems by identifying and taking into account new non-standard factors and dependencies. The use of the developed system in the IoT communication infrastructure will increase the level of information security and significantly reduce the risks of its loss.