Search

Lignocellulosic biomass is a vital resource for providing clean future energy with a sustainable environment. Besides lignocellulosic residues, nonlignocellulosic residues such as sewage sludge from industrial and municipal wastes are gained much attention due to its large quantities and ability to produce cheap and clean energy to potentially replace fossil fuels. These cheap and abundantly resources can reduce global warming owing to their less polluting nature. The low-quality biomass and high ash content of sewage sludge-based thermal conversion processes face several disadvantages towards its commercialization. Therefore, it is necessary to utilize these residues in combination with coal for improvement in energy conversion processes. As per author information, no concrete study is available to discuss the synergy and decomposition mechanism of residues blending. The objective of this study is to present the state-of-the-art review based on the thermal coconversion of biomass/sewage sludge, coal/biomass, and coal/sewage sludge blends through thermogravimetric analysis (TGA) to explore the synergistic effects of the composition, thermal conversion, and blending for bioenergy production. This paper will also contribute to detailing the operating conditions (heating rate, temperature, and residence time) of copyrolysis and cocombustion processes, properties, and chemical composition that may affect these processes and will provide a basis to improve the yield of biofuels from biomass/sewage sludge, coal/sewage sludge, and coal/biomass blends in thermal coconversion through thermogravimetric technique. Furthermore, the influencing factors and the possible decomposition mechanism are elaborated and discussed in detail. This study will provide recent development and future prospects for cothermal conversion of biomass, sewage, coal, and their blends.

In the recent worldwide coronavirus 2019 pandemic, a notable rise in pneumomediastinum and pneumothorax complications has been witnessed in numerous mechanically ventilated patients infected with severe acute respiratory syndrome coronavirus 2. Most cases have reported these complications as barotrauma from mechanical ventilation with COVID-19 disease. We aim to report three polymerase chain reaction-confirmed COVID-19 patients who developed pneumomediastinum and pneumothorax unrelated to mechanical ventilation. We originally analyzed 800 patients with COVID-19 disease at Orlando Regional Medical Center from March 1, 2020, to July 31, 2020, of which 12 patients developed pneumomediastinum and pneumothorax in their hospital course. Interestingly, three patients developed pneumomediastinum on chest imaging prior to intubation. We present these three patients, one female and two males, ages of 42, 64, and 65, respectively, who were diagnosed with COVID-19 disease through nasopharyngeal sampling tests with acute respiratory distress syndrome. Spontaneous pneumomediastinum and pneumothorax are potential complications of COVID-19 disease in the lungs unrelated to mechanical ventilation. This is similar to previous outbreaks of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) diseases. Further investigation is needed to define the causality of pneumomediastinum in nonintubated COVID-19 patients to define the incidence of disease.

Abstract Although numerous works have been done, most of the studies in fault diagnosis are limited to single fault type at a time. Majority of the works reported in the literature do not extend the diagnosis of the root cause of the fault for simultaneous faults specifically in the distillation column. However, an industrial system is susceptible to more than one fault at a time, which may or may not be interrelated. These faults not only reduce the diagnosis performance but also increase the computational complexity of the diagnosis algorithm. In this work, therefore, a multiple kernel support vector machine (MK‐SVM) algorithm is proposed to diagnose simultaneous faults in the distillation column. In the developed MK‐SVM algorithm, multilabel approach based on various kernel functions has been utilized for the classification of simultaneous faults. Dynamic simulation of a pilot‐scale distillation column using Aspen Plus® is used for generating data in normal and faulty operation. Eight different fault types are considered, including valve sticking at reflux and reboiler, tray upsets, loss of feed flow, feed composition, and feed temperature changes. In the classification of simultaneous faults, a combination of two, three, and four faults is introduced for the performance evaluation of the proposed MK‐SVM algorithm. The result showed that the proposed MK‐SVM has a high fault detection rate (FDR) of 99.51% and a very low misclassification rate (MR) of 0.49%. The MK‐SVM–based classification is better with the F1 score of >97% for all combinations of faults. Moreover, it is observed that the proposed MK‐SVM shows better fault diagnosis for single, multiple, and simultaneous faults as compared to other established machine‐learning algorithms.

Abstract The performance of a multistage centrifugal compressor is highly influenced by the ambient conditions, especially during the summer seasons; their capacity shrinks and thus the power requirement for compression will increase. The prime cause of these constraints is the interstage cooling limitations. This study simulates various suction conditions of a multistage compressor on Aspen HYSYS® and suggests its debottlenecking by making the suction temperatures comparable to winter seasons. This is achieved by installing an additional exchanger at the downstream of each interstage cooler, cooling down the gas further by using absorption refrigeration chillers. These chillers are powered up by the waste heat recovered from the exhaust steam coming from the prime mover, steam turbine, of the same compressor. This modification will save a considerable amount of power (663 kW), net savings (Gross Savings – OPEX: 72 289 $/y), and reduce the carbon footprint (954 ton/y) of the overall process.