Process Output Research Articles

Carbon monoxide inhibition on acidogenic glucose fermentation and aceticlastic methanogenesis

Syngas and CO-rich off-gases are key chemical platforms to produce biofuels and bioproducts. From the perspective of optimizing and up-scaling CO co-digestion with organic waste streams, this study aims at assessing and quantifying the inhibitory effects of CO on acidogenic glucose fermentation and aceticlastic methanogenesis. Mesophilic cultures were fed in two sets of batch assays, respectively, with glucose and acetate while being exposed to dissolved CO in equilibrium with partial pressures in the range of 0.25–1.00 atm. Cumulative methane production and microbial monitoring revealed that aceticlastic methanogenic archaea were significantly inhibited (2–20 % of the methane production of CO non-exposed cultures). The acidogenic glucose degrading community was also inhibited by CO, although, thanks to its functional redundancy, shifted its metabolism towards propionate production. Future work should assess the sensitivity of hereby estimated CO inhibition parameters, e.g., on the simulation output of a continuous syngas co-digestion process with organic substrates.

The state and error-feedback regulator problems for a boundary control catalytic cracking process

This research work is devoted to boundary control of a catalytic cracking reactor using state and error feedback regulators. The process mathematical model is governed by a set of partial differential equations (PDEs), for which infinite-dimensional representation and spectral properties of the system generator are employed to solve the regulation problems. The primary objective is to track a desired output reference trajectory in the presence of disturbances that are generated by a distributed parameter exosystem. Initially, a state feedback stabilizing regulator is designed to drive the process output towards the reference trajectory. The second objective is to develop a dynamic controller that employs the tracking error as input. The closed-loop plant is shown to be exponentially stable and the tracking error asymptotically approaches zero. The performances of the designed regulators are shown through numerical simulations.

Smart manufacturing platform based on input-output empirical relationships for process monitoring

Intelligent monitoring and maintenance protocols are undoubtedly crucial for improving manufacturing processes. Accordingly, machine learning techniques and predictive control models have been customized and optimized to account for the specific characteristics of the processes under investigation. In this context, the management of manufacturing processes in a “smart way” requires the development of specific models based on input-output empirical data. The aim of the proposed research was to develop an easily customizable application integrated into a milling process executed at the laboratory level. The application was designed to identify and record the operator, the order and the specific work sequences. It also supports the operator in setting processing parameters according to the type of work sequence to be performed. The application analyses specific process outputs, such as the wear growth on the inserts of the cutter in relation to the main input process parameters: depth of cut, feed rate, and spindle speed. This analysis is implemented by leveraging empirical evidence.

Neural Network Enables High Accuracy for Hepatitis B Surface Antigen Detection with a Plasmonic Platform.

The detection of hepatitis B surface antigen (HBsAg) is critical in diagnosing hepatitis B virus (HBV) infection. However, existing clinical detection technologies inevitably cause certain inaccuracies, leading to delayed or unwarranted treatment. Here, we introduce a label-free plasmonic biosensing method based on the thickness-sensitive plasmonic coupling, combined with supervised deep learning (DL) using neural networks. The strategy of utilizing neural networks to process output data can reduce the limit of detection (LOD) of the sensor and significantly improve the accuracy (from 93.1%-97.4% to 99%-99.6%). Compared with widely used emerging clinical technologies, our platform achieves accurate decisions with higher sensitivity in a short assay time (∼30 min). The integration of DL models considerably simplifies the readout procedure, resulting in a substantial decrease in processing time. Our findings offer a promising avenue for developing high-precision molecular detection tools for point-of-care (POC) applications.

Long-term complementary scheduling model of hydro-wind-solar under extreme drought weather conditions using an improved time-varying hedging rule

The frequent occurrence of extreme drought weather poses serious challenges to the complementary scheduling of renewable energy, including uncertain production processes, ineffective scheduling rules, and complex model solving, which further reduces the reliability of the power grid. To address these issues, a long-term complementary scheduling model of hydro-wind-solar (LCMHWS) considering hedging rules is proposed. First, to fully eliminate the uncertainty in monthly variables, Monte Carlo simulation and K-means clustering are sequentially absorbed into the model to process historical sequences and provide deterministic scenarios in which variable characteristics are conformed as input factors. Second, an improved time-varying hedging rule with strong drought resistance capability is proposed to optimize the complementary process of hydro-wind-solar and maximize the objectives of power generation and final energy storage. Last, NSGAII and membership function are adopted to search for the optimal Pareto solution for the uncertain multi-objective model. LCMHWS is validated for 7 hydropower and nearby wind and solar plants on the Lancang River in China. The results indicate that, while meeting the guaranteed output of the system, LCMHWS can increase the power generation and final energy storage by 14 % and 3 % under extreme drought conditions, respectively, and also smoothed the output process of power grid.

Analisis Recruitment Online pada PT. Tri Jaya Teknik Karawang

In the current era of globalization, ever-developing computer and internet technology always makes everything easier, including the recruitment process. The aim of this research is to find out, analyze and explain how online and offline recruitment is analyzed. The research design used in this research is descriptive research with a descriptive qualitative approach, and observations in the company environment, by interviewing several people in the company according to the data requirements. This research was carried out at PT. Tri Jaya Teknik Karawang which is located on Jl. Alternative Krajan II Ds. Warung Bambu Kec. Karawang. Researchers used the IPO (Input Process Output) method, interviews were obtained from the company's HR department. Online recruitment analysis uses Google Drive to collect data files and online tests, then uses Zoomeet for Keywords : Interview,Online, Technology,Recruitment

Process design and experimental study on drilling operations of a hybrid aluminum/carbon fiber reinforced polymer/titanium composite

ABSTRACT Carbon fiber reinforced composites (CFRP) are increasingly used in aviation and automotive industries. To enhance rigidity and fatigue resistance, aluminum (Al) and titanium (Ti) alloys are used together with CFRP materials. Drilling is a common method for manufacturing these hybrid composite stacks. This study examines the impact of process parameters, temperature, tool wear, thrust force, and torque on CFRP/Al/Ti hybrid composites during dry machining. Tests were conducted at various drilling speeds, feed rates, and stack orders using a solid carbide (WC) cutting tool. Results indicate that feed rate has a significant effect on the process outputs. The temperature remains low as the feed rate rises. The delamination factor is the highest in the Ti\\CFRP\\Al stack. In addition, delamination and temperature rise with increasing cutting speed. Finally, while the Al\\CFRP\\Ti stacking order performs poorly in terms of tool wear, it is measured more than 330 micrometers at the lowest feed rate.

Robust Bayesian target vector optimization for multi-stage manufacturing processes

This research focuses on optimizing multi-stage manufacturing processes using Bayesian optimization (BO) with a robust Expected Improvement (EI) acquisition function. The aim is to optimize towards pre-selected target vectors, not to just minimize or maximize a function. To achieve this, we minimize the Euclidean distance between the actual and target output vectors, which requires transforming the Gaussian surrogate model posterior distribution into a non-central χ2(NCχ2) distribution. Furthermore, the distance measure additionally uses aleatoric uncertainty estimates of the actual output vectors to achieve robustness. We use a cascaded method that also considers the optimization results of intermediate stages, whereby optimization results are propagated from the last stage towards the first stage in each optimization iteration. By considering intermediate process outputs and aleatoric effects, our approach provides a robust optimization method for multi-stage manufacturing processes. To validate our method and to evaluate its properties, we use two artificial use cases. Moreover, we evaluate our approach in an industrial multi-stage forging process for the manufacturing of a nickel basis superalloy turbine disk, where involved stages are represented by corresponding 2D finite element method (FEM) DEFORM simulations. Evaluation suggests that our approach is superior in optimizing multi-stage manufacturing processes by considering all stage outcomes, robust distance measures, and the use of appropriate uncertainty distributions.

A new analytical method for designing centralised PI controllers for unstable systems using a direct synthesis approach

This paper introduces a new analytical technique using a direct synthesis strategy to develop centralised proportional–integral (PI) controllers for multivariable processes. The current method design controller is focused on attaining the desired closed-loop response for multi input multi output (MIMO) processes that involve multiple time delays. The conventional multivariable PI controller is obtained by approximating the ideal multivariable controller by the Maclaurin series expansion. This is accomplished by choosing the desired closed-loop response order as the system order plus two. Subsequent analysis investigates the proposed method’s efficacy in designing multivariable PI controllers. Servo and regulatory problems studies were conducted to find the effectiveness of the proposed method and compared it with other methods recently reported in the literature, as well as assessment of performance indices like integral absolute error (IAE) and integral square error (ISE). The proposed controller's robustness is assessed by plotting the inverse maximum singular value against frequency, both input and output multiplicative uncertainties.

A novel method for the non-Gaussian wind pressure simulation based on autoregressive model and maximum entropy method

The simulation of wind pressures with non-Gaussian characteristics is a prerequisite to apply the Monte Carlo method in the structural wind-resistant reliability analysis. Due to its simplicity and accuracy, the autoregressive (AR) model has been widely used for the non-Gaussian process simulation. However, AR-based simulation methods perform poorly for the wind pressures with strong non-Gaussian characteristics. Moreover, they cannot reproduce the bimodal characteristics of some hardening wind pressures. To improve the simulation accuracy, a novel non-Gaussian simulation approach is proposed in this study based on AR model and maximum entropy method. In this approach, a set of formulas to determine the first ten moments of the input process as a function of the corresponding moments of the output process in the AR model have been firstly derived. Then, the probability distribution of the input process is reconstructed by the enhanced maximum entropy method. Finally, the translation process theory is introduced to simulate the non-Gaussian wind pressure process combined with AR model. Numerical results show the proposed method has higher simulation accuracy for the wind pressures with strong non-Gaussianity compared with the conventional AR-based method. Additionally, it can reproduce the bimodal characteristic for the wind pressures with bimodal distribution.

Energy Flow Analysis in Oilseed Sunflower Farms and Modeling with Artificial Neural Networks as Compared to Adaptive Neuro-Fuzzy Inference Systems (Case Study: Khoy County)

The evaluation of energy input and output processes in agricultural systems is a crucial method for assessing sustainability levels within these systems. In this research, the investigation focused on the input and output energies and related indices in sunflower farms in Khoy County during the agricultural year 2017–2018. Data were collected from 140 sunflower producers through specialized questionnaires and face-to-face interviews. Additionally, artificial neural networks (ANNs), specifically the multilayer perceptron, were employed to predict the output energy. The results revealed that a substantial portion of the total input energy was attributed to chemical nitrogen fertilizer (43.98%), consumable fuel (25.74%), and machinery (8.42%). The energy efficiency (energy ratio) in these agroecosystems was relatively low, measured at 1.57 for seed and 7.96 for seed and straw. These values should be improved. The energy efficiency in seed production was computed at 0.06 MJ·ha−1, while, for the combined seeds and straw, it was 0.57 MJ·ha−1. In particular, seed energy efficiency represents approximately 11% of the overall biological energy efficiency, highlighting that a substantial 89% of the produced energy is associated with straw. The proper use of this straw is crucial, as its improper handling could lead to a drastic decrease in overall efficiency. Furthermore, the explanatory coefficient (R2) and the mean absolute percentage error (MAPE) to predict the output energy with the best neural network were 0.94, and 1.77 for the training data, 0.97 and 1.55 for the test data, and 0.9 and 2.08 for the validation data, respectively; additionally, 0.97 and 0.42 were obtained by an ANFIS.

A comparative study on ensemble soft-computing methods for geothermal power production potential forecasting

Many developed countries are increasingly interested in renewable energy sources (RESs) as a result of environmental changes and the depletion of fossil fuels in recent years. Since geothermal energy can be used as both a source of electricity and heat, it occupies an important spot among renewable energy sources. In this study, soft-computing ensemble models (SCEMs) based on supervised deep neural network (SDNN) models supported by the forward stepwise regression (FSR) method are used in estimating the power generation from geothermal resources. Outputs of the FSR process led SDNN phase. Adaptive Moment Estimation (ADAM) and Nesterov-accelerated Adaptive Moment Estimation (NADAM) methods were used to optimize SDNN models. For the daily power generation, the best performance has been shown by the model of SDNN optimized using ADAM optimizer with a coefficient of determination (R2) of 0.9807 and root mean square error (RMSE) of 0.0466, respectively.

Technology of extraction of fuel mass from ash and slag waste in Ukraine

Purpose. Development of a technological scheme for the processing of ash and slag waste from coal-fired thermal power plants. The methods. The ash and slag dumps of thermal power plants are made up of the main components - slag, ash and unburned coal particles, drawing up an appropriate scheme for the extraction of coal particles allows the development of an effective scheme for the processing of ash and slag outputs of thermal power plants. The slag is separated from the fly ash by classification on a screen. Next, the method of extracting fuel mass from fly ash is used - the flotation method, based on the selective fixation of coal particles on the surface of air bubbles in the flotation chamber, with subsequent extraction of coal particles into a foam product. The mineral part of the ash cleaned from the fuel mass is ready for use in the road construction industry. Findings. A technological scheme for the processing of ash and slag waste of coal-fired thermal power plants has been developed, which makes it possible to obtain slag, high-quality coal concentrate, and the mineral part of ash. The originality. For the first time, a mathematical model was developed for forecasting indicators of enrichment of ash and slag waste of coal-fired thermal power plants by the flotation method. To predict beneficiation indicators, it is necessary to use three disclosure values: the initial ash content of the coal fraction (Aпd), the number of coal grains (PРЗ), the number of rock grains (PНЗ), provided that the separation characteristics of the beneficiation equipment are experimentally determined in advance. On the basis of the developed mathematical model of forecasting indicators of ash slag waste enrichment, an appropriate flotation scheme for effective extraction of fuel mass was drawn up, which made it possible to develop a technological scheme for processing ash slag waste of coal-fired thermal power plants. Practical implementation. The developed technological scheme for the processing of ash and slag waste makes it possible to obtain slag with a yield of 3.11% suitable for filling industrial roads, coal concentrate with a yield of 25.75% with an ash content of 21.80% can be used as a secondary fuel for thermal power plants or as a raw material for production solid fuel briquettes, as well as the mineral part of ash with a yield of 71.14% with an ash content of 98.10%, which can be used in the road construction industry.

Study protocol OKRA: orientation compass for the preparing, delivering and following up on breaking bad news conversations in paediatric oncology

IntroductionIn paediatric oncology, ‘breaking bad news’—BBN—like cancer diagnosis is perceived as particularly challenging. Enabling a trialogue between children with their relatives and health professionals requires profound communication skills. Lacking the...

Artificial neural network modelling of natural gas dehydration process

A multi-input–multi-output artificial neuron network (MIMO-ANN) model has been developed for process monitoring and improvement on a natural gas glycol dehydration process. The MIMO-ANN model was based on a steady-state process simulation model constructed in commercial software Aspen HYSYS. A set of training data was generated with the converged simulation model for the training of the MIMO-ANN model in Python. The process input of the model includes lean glycol recirculation rate and purity along with wet gas inlet pressure. On the other hand, the process output considered includes dehydrated gas water and aromatics content, dehydrated gas hydrate formation temperature and water dew point, stripping gas flow rate as well as reboiler duty. The overall mean squared error (MSE) of the MIMO-ANN model was calculated as 1.79. The best-fit line that highly overlaps with a 45° diagonal line is constructed with a correlation coefficient (R2) score of more than 0.999 for all studied process output testing datasets. The mean absolute percentage error (MAPE) of the predicted outputs is generally less than 1%, except for dehydrated gas water dew point (16.63%) and stripping gas flow rate (11.55%), due to error predictions attempted on pseudo-zero values. This successfully displays an exceptional predictive performance of the MIMO-ANN model developed in this work which can be further deployed as an online dashboard for real-time monitoring tool.

Concentration, market imperfections, and interbranch organization in the Italian processed tomato supply chain

AbstractThe increase in market concentration and market power in the food supply chain is an issue of concern globally. This study focuses on an analysis of market imperfections in the Italian processed tomato food supply chain by considering changes in its supply chain governance from 2006–2018. The identification of the degree of non‐competitive behavior is based on the derived mark‐down and mark‐up models using the latest developments in stochastic frontier methodology. The estimated models reveal some degree of non‐competitive behavior in the input as well as in the output processing market. However, in consideration of the results on the supply chain governance during the study period, we argue that the establishment of an Interbranch Organization (IBO) could create fairly stable long‐term food supply chain relationships benefitting all IBO members and in particular farmers despite the significant change in concentration levels in the Italian tomato processing sector after 2015.

Adaptive Critic Design for Event-Triggered Tracking Control in Discrete-Time on Linear Systems with Observers

This research pioneers an innovative methodology for the bespoke manufacturing of SquidSkin safety products, meticulously tailored to the distinct requisites of diverse industries encompassing medical, military, and defense sectors. The triple DES algorithm is a three instance of DES on same plain text in blocks of 64 bits and converts them to cipher text using keys of 168 bits. The triple DES encryption process creates cipher text, which is an unreadable, effectively indecipherable conversion of plaintext data, the version of information that humans can read and understand. The output of the encryption process, the triple DES cipher text, cannot be read until a secret triple DES key is used to decrypt it. The delineation of industry- specific manufacturing parameters mandates a nuanced approach to material composition and production processes, culminating in a paradigmatic advancement in safety product customization. Integral to this methodology is a rigorous analysis of industry-specific requirements, guiding the selection of optimal material compositions. This selection is underpinned by a meticulously designed material testing phase, ensuring adherence to exacting industry standards and performance benchmarks. Augmenting this paradigm is an imperative emphasis on fortifying the security framework underpinning the handling of proprietary manufacturing data. The proposal introduces an advanced cryptographic architecture, featuring encryption and decryption protocols. Approach assures heightened efficiency, unwavering reliability, and the preservation of proprietary information across varied industry sectors.

Model-based process optimization of black soldier fly egg production.

Black soldier fly (BSF) larvae (Hermetia illucens) serve as a valuable protein source for animal feed. Limiting factors in the industrial rearing of BSF are the reproduction process and egg output. Studies indicate the potential to shorten preoviposition time and increase egg output through better utilization of environmental variables, such as temperature and light, in industrial settings. Excessive stimulation, however, can lead to stress, elevated production costs, and reduced egg numbers, emphasizing the need for a delicate balance. This study addresses these challenges by investigating controlled manipulation of environmental variables to stimulate mating and enhance egg production, thereby developing a comprehensive model encompassing the adult fly life cycle, mating, and egg production. Model parameters were fitted using literature data, and the model's plausibility was tested through simulations. Using the model and optimal control methods, the calculated dynamic trajectories for environmental variables when compared to the standard approach in a constant environment demonstrated higher output and shorter production cycles at reasonable energy costs. Applications for this model-based optimization are demonstrated for various scenarios, highlighting the practical utility and versatility of the developed model. This study contributes valuable insights for improving rearing practices of BSF through environmental stimulation, offering potential advancements in egg production efficiency and overall sustainability.

The Output Signal-to-Noise Ratio and Speech Perception in Noise: The Effect of Multichannel and Free-Channel Hearing Aids

Background and Aim: The output Signal-to-Noise Ratio (SNR) is one of the essential factors in hearing aid benefits. There is limited evidence regarding SNR improvement by the Channel-Free (CFHA) and Multi-Channel Hearing Aid (MCHA) and the speech understanding in noise through them. This study aimed to investigate the extent to which output SNR was modified by CFHA and MCHA processing and the variation in aided speech recognition abilities with a change in output SNR. Methods: Thirty-six participants aged 50–65 years were included. A chosen CFHA and MCHA were used to obtain the output SNR and sentence recognition in noise in four different processing algorithms (linear, linear+noise reduction, WDRC, WDRC+noise reduction). Hagerman’s phase inversion technique was used to measure the attenuation of noise and, in turn, to obtain the output SNR of the hearing aid. Results: In all hearing aid processing algorithms among those with normal hearing and people with hearing loss, the output of CFHA revealed higher attenuation values than that of MCHA. There was a significant effect of the hearing aids and processing algorithms in both normal and individuals with hearing impairment on the mean SNR. Further, multiple linear regression analysis results showed that whether the hearing is channel-free or multi- channel significantly predicted speech recognition scores, while output SNR and processing algorithms did not. Conclusion: The signal processing algorithms in CFHA had greater noise attenuation values, better output SNR, and speech recognition scores, showing an advantage over the modern MCHA among individuals with hearing impairment. Keywords: Channel-free hearing aids; multi-channel hearing aids; sensorineural hearing loss; speech recognition

Numerical analysis of machinability and surface alterations in cryogenic machining of additively manufactured Ti6Al4V alloy

Metal additive manufacturing (AM) technology has been utilized in many industries including automotive, aerospace, and medical. AM Ti6Al4V (Ti64) alloy is highly noticed for production of medical instruments such as dental implants and the machining process is mostly needed during the production or post-processing of these components. Numerical model, as a powerful tool, can be efficiently used for analyzing the machining process. A customized model was employed using a user-written subroutine in this work to evaluate machinability and microstructural changes in cryogenic machining of AM Ti64 alloy. For this purpose, the microstructural changes were simulated as the new numerical outputs. The numerical results of cutting forces, temperature, nano-hardness, and alpha lamellae thickness (grain size) were successfully verified by corresponding experiments from literature. Then, the impact of tool geometry (including rake and clearance angles, cutting edge radius, and nose radius) on the machinability performance was examined. It was found that, the variation of clearance and rake angles were more effective on depth of the hardened layer compared to the other parameters. Thickness of alpha lamellae phase near the machined surface and depth of the affected layer by nano-hardness changes were changed from 0.9 to 1.58 µm, and from 18 to 40 µm, respectively. Overall, it was concluded that the variation of insert positioning made by tool holder (change in rake and clearance angles) was an effective parameter on the process outputs when machining AM Ti64 alloy.