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The use of control systems is necessary for safe and optimal operation of industrial processes in the presence of inevitable disturbances and uncertainties.Plant-wide control (PWC) involves the systems and strategies required to control an entire chemical plant consisting of many interacting unit operations.

Fast neutron activation analysis (FNAA) offers a new way to determine the bulk composition of Heusler alloys. Fast neutrons penetrate deep in the alloys, allowing to quantify the bulk elemental composition thanks to the characteristic gamma-rays emitted by the excited elements. In this work, a fast neutron generator (MP320) and a low-background high-efficiency well-type Ge gamma spectrometer are used to quantify the amount of Mn, Fe, Al, Si, and Sn in manufactured Heusler alloys. The alloy bulk composition is compared with the surface composition (micrometer depth range) obtained by classical energy dispersive X-ray analysis, demonstrating the interest of FNAA.

This paper investigates the fast chemical diffusion limit from a parabolic-parabolic Keller-Segel system to the corresponding parabolic-elliptic Keller-Segel system by constructing approximate solutions with an appropriate order via an asymptotic expansion. Nonlinear stability of the precise initial layer is characterized with an exact convergence rate by using basic energy method.

This review deals with the issue of operational coal quality control using instrumental nuclear–physical methods. The existing traditional method of coal testing, characterized by high labor intensity and low representativeness, cannot serve as a basis for operational management of mining and processing processes. Instrumental nuclear–physical methods are free from these drawbacks; they are based on various processes of interaction of gamma and neutron radiation with substances. The main modifications of instrumental methods using gamma radiation are discussed: backscattering, forward gamma scattering, gamma absorption, gamma annihilation, and natural gamma activity. Various modifications of gamma methods are related to the energy of the primary and recorded radiation, the prevalence of a particular interaction process, the depth of the method, characteristics of the test object, the measurement geometry, and the other factors. The features of gamma methods are described in the context of the tasks being solved, interfering factors (variations in the bulk density, the moisture content, and the elemental composition), and methodological approaches for increasing the sensitivity and accuracy of the coal quality assessment. The variety of modifications of neutron methods is associated with irradiation of the analyzed coal with neutrons of different energies and detection of secondary gamma radiation arising from neutron activation of elements, inelastic scattering of fast neutrons, and radiative capture of thermal neutrons by the elements composing the coal. The methodological features of neutron activation, the neutron–gamma method of inelastic scattering and radiative capture are considered in the context of elemental analysis for Al, Si, S, Ca, Fe, H, C, and O and determining the ash content of coal in general. The main trends of the instrumental quality control are highlighted and recommendations are given for their use depending on the metrological characteristics and physical and chemical properties of the control object. The gamma-albedo method with registration of X-ray fluorescence of heavy gold-forming elements is the most promising for express analysis of powder samples. To test coarse coal in large amounts, multiparameter neutron methods are needed that comprehensively utilize high-precision equipment and instrumental signals from carbon, oxygen, and major ash-forming elements.

Advancing neural interfaces requires implantable devices capable of long-term electrical and chemical monitoring. “All”-glassy carbon (GC) microelectrode arrays (MEAs), in which both electrodes and interconnects are formed from homogeneous GC layer, offer integrated chemical sensing and electrophysiological recording, while enhancing electrochemical durability by eliminating metal components. To guide the development of high-resolution, double-layer “all”-GC MEAs for higher-density architectures, this study systematically investigates GC as both an interconnect and neurochemical sensing material, with particular focus on the effects of double pyrolysis on structural integrity, interconnect resistance, and microelectrode performance. Sheet resistance was analyzed across films of varying thicknesses, and interconnect geometry was evaluated. Raman spectroscopy and X-ray diffraction characterized graphitization and crystallinity, while fast-scan cyclic voltammetry (FSCV) assessed dopamine and serotonin detection. A 48% reduction in the thickness of once-pyrolyzed GC corresponds to a 63% increase in its sheet resistance. A double pyrolyzed GC trace has about 50% higher sheet resistance than a single-pyrolyzed GC trace of the same thickness. Double pyrolysis caused approximately 20% shrinkage in the GC layer. Compared to Cr/Au/Pt traces, GC interconnects had higher resistance at 1–3 µm widths but approached metal-like performance at 5–10 µm. Importantly, the second pyrolysis cycle preserved structural integrity and FSCV sensitivity. These analyses advance our understanding of GC’s electrical and sensing properties, providing critical insights for optimizing compact multilayer devices in next-generation “all”-GC-MEAs.

Fluorinated greenhouse gases (FGGs) are classified as worldwide pollutants and have a high global warming potential compared to other greenhouse gases. Detecting the existence and concentration of new and older refrigerant gases is crucial for assessing system functionality and determining whether they can be recycled or need to be disposed of. Additional justifications for the necessity of quantitative measurements of these gases include the manufacturing of air conditioning components; leak detection is conducted to ensure they are free of leaks. Classical laboratory Fast Fourier transform spectrometers enable the detection and measurement of substances while being delicate, unwieldy, and costly, and typically requiring a skilled technician to operate them. For the estimation of refrigerants in the field, a portable, user-friendly, and cost-effective detection device must be deployed. This article provides an in-depth analysis of the categorization of refrigerant gases using an Internet of Things (IoT) gas detection device. The functionality in effectively differentiating between important refrigerant gases, like R-32 and R-134a, with low delay, is demonstrated through practical tests. With the portable device, this study utilizes Fourier-Transformed infrared spectra measured from the refrigerants R-32 and R-134a, collected using a custom-made 3D-printed tubular reactor equipped with two BaF2 windows, suitable for use in the beamline of a Bruker IR Spectrometer. Calibration was performed by exposing the infrared sensor to controlled gas environments with varying amounts of refrigerant gases using accurately produced gas mixtures. Following the on-field analysis of the reclaimed refrigerants, the obtained data was immediately processed, and both the data and the results were uploaded to an IoT platform, making them available to business-to-business (B2B) clients. The functionality of the device is demonstrated.

The pattern of micro-electricity production of simple two-chamber microbial fuel cells (MFC) was monitored in this study. Piggery wastewater and anaerobic sludge served as fuel and inocula for the MFC, respectively. The output power, including voltage and current generation, of triplicate MFCs was measured using an on-line monitoring system. The maximum voltage obtained among the triplicates was 0.663 V. We also found that removal efficiency of chemical oxygen demand (COD) and biochemical oxygen demand (BOD) in the piggery wastewater was 94.99 and 98.63%, respectively. Moreover, analytical results of Fast Fourier Transform (FFT) demonstrated that the output current comprised alternating current (AC) and direct current (DC) components, ranging from mA to μA.

As chemical mechanical planarization (CMP) enables local and global planarization over a wafer surface by the combined effects of chemical and mechanical interactions, process monitoring is becoming an increasingly important in-situ methodology for process control. According to the materials and process, signal characteristics were distinguishable between material and process. In this study, an acoustic emission (AE) sensor was used to measure the abrasive and molecular-scale phenomena during CMP. An AE signal was acquired using rotational equipment and adapted to two types of equipment. First, a wireless AE system consisting of wireless modules using Bluetooth was used. This system was suitable for acquiring signals in rotational equipment. However, a wireless AE system could be acquired with only Root Mean Square(RMS) signals. Second, mercury slip-ring (wired) AE systems that were suitable for rotational equipment and the acquisition of raw signals were used. The acquired raw signals could be analyzed by a Fast Fourier Transform (FFT) for abrasive and molecular-level phenomena in the CMP process. The AE signal parameters including the AE RMS, frequency, and amplitude were analyzed for abrasive and molecular-level phenomena in the CMP process. The authors analyzed the AE signals for changes in the materials and CMP process.

The problems of parametric representation of the initial formation and the subsequent evolution of fast ice in freezing seas are discussed within the framework of the model of marine ice cover evolution. The mathematical form of this representation takes into account the processes of transformation of sea ice formations in open water areas in coastal regions into fast ice during its formation and inverse processes at the stage of its destruction. The parametric identification of the model was based on samples of long-term distributions of ice cover characteristics in the Sea of Japan, as well as the distributions of air temperature and wind speeds over sea water area. The model can be used to predict the state of fast ice in the ice cover of the Sea of Japan.

When Taco Bell surveyed its customers on their 4 most important expectations, the company found that it had been concentrating on the wrong things. What its customers wanted was fast and correct service, a clean dining area, and hot food. Taco Bell was able to make changes that increased its share of the Mexican fast food market from 40% to 70%. Taco Bell is an example of the usefulness of benchmarking outside the chemical industry, according to Jim Northam of Gemini Consulting. The important thing to note about Taco Bell's overall strategy was that the company got out ahead of the competition with a customer survey and was willing to clear its corporate mind of all assumptions and uncertainties that stood in the way of being what it wanted to be. According to Northam, the key to business transformation is identifying and dealing with issues that stand in the way of a company's realizing its vision of where it wants to be in a market. Other successful business transformations are discussed.