查看更多>>摘要：Valuable renewable carbon biorefinery products can be obtained by using agro-residual biomass as a feedstock. Bio-oil, gas, and char products can be obtained from Microwave-assisted pyrolysis (MAP) by converting agro-residual waste. In MAP, the process variables like microwave power, temperature, heating rate, raw materials, susceptors, and catalysts play an important role to alter the product spectrum. The temperature, heating rate, and pyrolysis time can be tuned to obtain the desired products during biomass decomposition. The obtained carbonaceous products can be used as intermediated feedstocks to synthesize a variety of end products. Hence, in this review, the application of MAP for the conversion of agro-residual waste is discussed. Special focus is given to the interaction of microwaves with susceptors. This manuscript provides background, current status, progress, and future scope of MAP technology for waste valorization. The objectives of the review are to address (i) The necessity of environmental protection, (ii) The role of biorefinery in the biomass conversion, (iii) The advancements in the MAP for the resource recovery, (iv) The mechanism of heat generation from microwaves, (v) The effects of process parameters, susceptors, and catalysts in MAP, (vi) The interactions of biomass and susceptors during the pyrolysis, (vii) The formation of valuable renewable carbon products and (viii) The future scope and challenges for the integration of MAP in solid waste management.
查看更多>>摘要：The interaction of perfluorooctanoic acid (PFOA) with anaerobic and aerobic microorganisms was investigated to explore the effect of PFOA on microbial community evolution, carbon metabolism and nitrogen metabolism, and to a provide reference for treatment of wastewater containing PFOA. Compared with phase I (PFOA 0 mg L~(-1)), the average COD removal rate of the anaerobic sequencing batch reactor (AnSBR) in phase II, III and IV (5-20 mg L~(-1)) decreased by 8.85%, and that of the aerobic sequencing batch reactor (ASBR) decreased by 8.31%. However, PFOA had little effect on ammonia nitrogen (NH4~+-N) removal at the preset concentrations and the average removal rate of NH4~+-N in the system was 97.90 ± 0.59%. The average removal of PFOA in the AnSBR and ASBR decreased from 78.30% and 86.36% (phase I), to 18.77% and 19.25% (phase IV), respectively, indicating that the removal of PFOA was mainly dependent on the adsorption of microorganisms in the initial the experiment. 3D excitation-emission matrix results showed that PFOA affected the functional groups of extracellular polymeric substances, especially the red shift of fulvic acids along the Em axis. Meanwhile, high concentration PFOA could reduce the activities of acetate kinase and coenzyme F420 in the sludge, thereby affecting the metabolism of carbon and methane by microorganisms. When the concentration of PFOA was 20 mg L , the abundances of hydrogenotrophic methanogens such as Methanospirillum (AnSBR phase I-IV: 24.07%-27.43%) and Methanobacterium (AnSBR phase I-IV: 15.06%-21.27%) exceeded that of methylotrophic methanogens, indicating that the existence of PFOA changed the methane metabolism pathway.
Muhammad Ikhsan TaipabuKarthickeyan ViswanathanWei Wu
24页
查看更多>>摘要：Hydrogen is a clean alternative fuel without carbon gas emission. This paper presents a critical evaluation of the different methods available for generating hydrogen from various feedstocks. The advantages and disadvantages of each process are discussed deeply by recent literatures. Steam reforming of fossil fuels (SRF) has been proved as an attractive method and commercialized on the larger scale. However, CO2 emission that produced during the process is critical issue by this method and therefore, CO2 capture, and storage/utilization technology are required. Besides, water splitting can produce ultra-purity hydrogen and oxygen as byproduct, but this method cannot be competed with SRF because of its expensive costs. The only possible to reduce this gap is by using solar energy with low-cost as energy source for water splitting and carbon taxes are imposed by the government to support research and development. Hydrogen production derived from biomass through gasification and py-rolysis currently shown an economic visibility and expected compete with available technology in the future. Moreover, by utilizing membrane reactor and integrated with a cheaper solar energy could significantly improve biomass-to-hydrogen conversion.
Swellam W. SharshirA.W. KandealAlmoataz M. Algazzar
10页
查看更多>>摘要：In this work, pyramid solar still performance was enhanced by using evacuated tubes, external condenser, nanoparticles, and ultrasonic foggers. Experimental measurements were recorded and analyzed to investigate the influence of combining nanoparticles and ultrasonic foggers to the modified pyramid solar still (MPSS) by evacuated tubes and external condenser compared with the conventional pyramid solar still (CPSS). Compared to CPSS, the results showed that MPSS by six evacuated tubes and an external condenser has higher freshwater output, energy efficiency, and exergy efficiency by 91.09%, 18.48%, and 45.26%, respectively. While, adding 1 wt% carbon black (CB) nanoparticle to this MPSS can change these percentages to 132.86%, 28.22% and 75.43%. Moreover, adding three ultrasonic foggers with nanoparticles changed the enhancement percentages to 162.15%, 34.26%, and 81.51%. The economic analysis showed the effectiveness of the suggested modifications, and the cost per liter of freshwater can be decreased by up to 32.04% compared with CPSS. Depending on the environmental analysis, the highest environmental parameter (CO2 emissions) was 1.379 ton-CO2/year for the MPSS by all proposed modifications. So, all proposed modifications can be considered effective according to environmental, exergoeconomic, and enviroeconomic point of view.
查看更多>>摘要：Due to the recent increase in drilling operations complexity, the frequency of undesirable downhole events occurring while drilling a well is in ascend trend leading to substantial growth in non-productive time. Consequently, overall drilling costs become sky-high, a moment in which earlier and precise detection of the downhole drilling problems becomes a crucial factor in cost reduction. This paper presents an intelligent algorithm that can automatically analyze real-time drilling data and accurately detect and verify the presence of the most common downhole drilling problems upon their effective inception, which allows corrective measures to be applied at the appropriate time, resulting in a reduction of the negative impact and the associated cost of the detected downhole failure. The presented algorithm relies on constructing a risk predictive window by integrating a stochastic model with a data-driven model driven from real-time data of the surface and /or subsurface drilling parameters to detect the downhole problems. The process starts by building predictive models for predefined drilling parameters. Based on the natural distribution of the Residual Errors (REs) obtained while building the productive model, the best probability model that fits the REs data is picked. The statistical properties of the selective probabilistic models and the real-time predictive values of the predefined drilling parameters are used to generate multiple dimensional risk predictive windows;; the indicated risk predictive window could have one or two dimensions, depending on the number of pre-built productive models. The downhole drilling problem is detected by observing and comparing the real-time measured value of the used drilling parameters with the risk predictive window;; consecutive data points located outside the risk predictive window are considered abnormal. An alarm is triggered when the number of sequent outliers reaches a predetermined boundary. The developed algorithm was tested on a historical drilling dataset in which different downhole incidences occurred. The results show that the algorithm successfully detected all of the events at an average of 120 min before the officially recorded time.
查看更多>>摘要：The objective of this study was to improve the efficiency of microbial desulphurization of sulfide ore by pre-treatment with ultrasonic and microwave techniques. Different combinations of ultrasound and microwave were used to pretreat the sulfide ore and compare the microbial desulphurization effect of the treated ore and the raw ore. The experimental results show that ultrasonic and microwave can effectively improve the efficiency of bacterial desulphurization, whereas the pretreatment method of ultrasonic followed by microwave has the best enhancement effect on bacterial desulphurization of sulfide ores. The best experimental combination for microbial desulphurization is ultrasonic power of 300 W, ultrasonic action time of 50 mins and microwave power of 500 W, microwave action time of 20 s. In subsequent self-heating simulations of sulfide ore heaps, the optimal ultrasonic and microwave combination was used to pretreat the ores and the two-dimensional temperature field of the heaps was reconstructed. The results further show that the microbial desulphurization efficiency is increased when the sulfide ore is pretreated with ultrasonic followed by microwave. The purpose of inhibiting the self-heating reaction and preventing spontaneous combustion of sulfide ore was achieved.
Seyed Amir Hossein Seyed MousaviSeyed Mojtaba SadrameliAmir Hossein Saeedi Dehaghani
19页
查看更多>>摘要：In this study, municipal polymer wastes that were no longer recyclable and were previously buried underground according to the usual municipal waste management program were converted to liquid fuel by catalytic pyrolysis technique. In the first step, zeolite Y catalyst was used to improve the quality of liquid fuel. In the second step, MIL-53 (Cu) was incorporated onto zeolite and pyrolyzed in nitrogen in the atmosphere. The analysis shows that clusters of carbon nanopores with a copper core, and its oxides were deposited on the zeolite. For both types of catalysts, the crystallization time of zeolite was investigated, and it was found that this leads to the synthesis of samples with different percentages of crystallinity. For each test, the liquid fuel produced was divided into four cuts: gasoline, jet fuel, diesel, and wax. The test results in a fixed bed reactor for every twelve samples of catalyst and their effect on the efficiency of different sections of liquid fuel show a significant improvement in the desired product. The properties and morphology of the catalysts were investigated. It was found that at 400 °C and a crystallization time of 18 h for support with 76.62% crystallinity, gasoline production efficiency will be 37.00%. At 500 °C and low crystallinity, the tendency of reaction to produce jet fuel with a maximum efficiency of 48.64%. Furthermore, the physical properties of each cut of liquid fuel and their comparison with the reported values indicating the appropriate qualities of the produced fuel were evaluated. In the optimal state, the octane number of the produced gasoline is 93.5 and its pour point is 41 °C. Also, a jet fuel with an octane number of 43.2 and a flashpoint of 69 °C has been obtained. In the case of diesel, the octane number and its viscosity have reached 46 and 2.407 cp, respectively. Examining the results obtained from GC MS, it was found that the zeolite catalyst modified by Diels Alder mechanism and branching will improve the quality of liquid fuel and on the other hand, will cause the cracking of the wax compounds and reduce their percentage in product analysis.
查看更多>>摘要：Real-time and accurate leakage detection of natural gas gathering pipelines is critical to the safe and reliable operation of the gas and oil industry. Modern data-driven fault detection and diagnosis techniques have recently gained increasing attention because model-based techniques are practically prohibitive. However, most existing data-driven leakage detection approaches are obtained through supervised learning, which requires a substantial set of labelled data. Especially in real-world scenarios, leakage samples are rare. To reduce the dependence of the leakage detection method on leakage data and make full use of numerous normal datasets generated under normal working conditions, we propose a semi-supervised leakage detection method which consists of two components: an improved long short term memory autoencoder (LSTM-AE) network and one-class support vector machine (OCSVM). The LSTM-AE is first trained to learn the intrinsic features of a normal dataset of pipeline parameters which are multivariate time series. The OCSVM is then applied to calculate the score which is used to infer leak existence. The performance of the proposed method is evaluated on the real natural gas gathering pipelines, and the results confirm that the proposed method achieved 98% accuracy and 99% AUC (Area Under Curve) in a real-life dataset.
查看更多>>摘要：The performance of sociotechnical elements varies owing to a wide range of endogenous and exogenous influencing factors. These are called uncoupled variability as per Safety-II. The uncoupled variability has drawn rare attention, despite its vital importance in major accidents analysis as per Safety-I and Safety-II paradigms. Accordingly, as the first attempt, this study proposes a systematic model to analyze performance variability in human, organizational, and technology-oriented functions caused by various variability shaping factors (VSFs). The model contains three main phases. First, a FRAM (Functional Resonance Analysis Method) - driven Human-Organization-Technology Taxonomy is developed. Subsequently, Dempster - Shafer Evidence theory is employed to elicit knowledge under epistemic uncertainty. The proposed causation model is integrated into Dynamic Bayesian Networks to support decision-making under aleatory uncertainty. Finally, a criticality matrix is developed to evaluate the performance of the system functions to support decision-making. The proposed model is built considering the advanced canonical probabilistic approaches (e.g., Noisy Max and Leaky models) that address the critical challenges of incomplete and imprecise data. The proposed dynamic model would help better understand, analyze, and improve the safety performance of complex sociotechnical systems.
查看更多>>摘要：Geothermal energy-driven systems with integrated waste heat recovery units such as the use of fuel cells and thermoelectric module can help to improve the renewable energy contribution in the energy mix. Data-driven optimization can improve their economic and environmental performance and their macro-projection can help in the achievement of net-zero plans. This article extends the use of a framework containing the usage of data modeling and artificial intelligence to conduct different optimization scenarios of the geothermal-driven energy system. It includes the improvement of the economic, exergetic, energetic, and environmental performance through the development of various optimization scenarios. This is done through the development of an extensive thermodynamic model and validation based upon energy, exergy, economic, and environmental evaluations. Different machine learning techniques are adapted for digital twinning of the six performance indicators as a function of nine design variables including operational, source, and economic variables. It is shown that the artificial neural network offers the best statistical fit as compared to the other machine learning techniques including RMSE: 0.1768, R~2:0.9999, MSE:0.0312, and MAE:0.1107 for the total work output. Energy-efficient design has yielded a total work output of 1044.86 kW, with a first law efficiency of 0.3322. The economic design offers the lowest cost of electricity at only 34.004 $/hr. The sensitivity analysis has shown that the following parameters are the most sensitivity: turbine inlet temperature (18.19%) and pressure (18.23%), geothermal inlet temperature (16.34%) and pressure (18.00%), and the ammonia water concentration at the inlet of separator (15.96%).
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