Process Output Research Articles

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.

Impaired speech input and output processing abilities in children with cleft palate speech disorder.

Cleft lip and palate is one of the most common oral and maxillofacial deformities associated with a variety of functional disorders. Cleft palate speech disorder (CPSD) occurs the most frequently and manifests a series of characteristic speech features, which are called cleft speech characteristics. Some scholars believe that children with CPSD and poor speech outcomes may also have weaknesses in speech input processing ability, but evidence is still lacking so far. (1) To explore whether children with CPSD and speech output disorders also have defects in speech input processing abilities; (2) to explore the correlation between speech input and output processing abilities. Children in the experimental group were enrolled from Beijing Stomatological Hospital, Capital Medical University, and healthy volunteers were recruited as controls. Then three tasks containing real and pseudo words were performed sequentially. Reaction time, accuracy and other indicators in three tasks were collected and then analysed. The indicators in the experimental group were significantly lower than those in the control group. There was a strong correlation between speech input and output processing tasks. The performance of both groups when processing pseudo words in the three tasks was worse than that when dealing with real words. Compared with normal controls, children with CPSD have deficits in both speech input and output processing, and there is a strong correlation between speech input and output speech processing abilities. In addition, the pseudo words task was more challenging than the real word task for both groups. What is already known on the subject Children with cleft lip and palate often have speech sound disorders known as cleft palate speech disorder (CPSD). CPSD is characterised by consonant errors called cleft speech characteristics, which can persist even after surgery. Some studies suggest that poor speech outcomes in children with CPSD may be associated with deficits in processing speech input. However, this has not been validated in mainland China. What this paper adds to existing knowledge The results of our study indicate that children with CPSD exhibit poorer performance in three tasks assessing speech input and output abilities compared to healthy controls, suggesting their deficits in both speech input and output processing. Furthermore, a significant correlation was observed between speech input and output processing abilities. Additionally, both groups demonstrated greater difficulty in processing pseudo words compared to real words, as evidenced by their worse performance in dealing with pseudo words. What are the potential or actual clinical implications of this work? The pseudo word tasks designed and implemented in our study can be employed in future research and assessment of speech input and output abilities in Chinese Mandarin children with CPSD. Additionally, our findings revealed the significance of considering both speech output processing abilities and potential existence of speech input processing ability for speech and language therapists when evaluating and developing treatment options for children with CPSD as these abilities are also important for the development of literacy development.

Hydrogel-based flexible degradable triboelectric nanogenerators for human activity recognition

The development of Internet of Things (IoT) and Artificial Intelligence (AI) technologies has led to an increased demand for wearable electronic devices that can be used in a variety of contexts. Hydrogel sensors have been regarded as an ideal material for the fabrication of multifunctional flexible wearable devices. However, it is difficult to create hydrogel sensors with ideal electrical and mechanical properties. In this study, a conductive hydrogel prepared by a simple method has been introduced. The hydrogel was immersed in a binary solvent of glycerol, water and sodium citrate to form a transparent, long-term stable and highly conductive gelatin-NaCl organohydrogel (GNOH). A stretchable and durable gelatin Ecoflex triboelectric nanogenerator (GE-TENG) was created by combining an organic hydrogel with Ecoflex elastomer. It has high electrical and mechanical performance, with an open-circuit voltage of 142 V, current of 3.8 μA, power output of 19.36 μW and response time of 85 ms. Moreover, the single-electrode mode of the GE-TENG provides high electrical output for powering portable electronic devices and can capture biomechanical energy in temperatures as low as −20 °C. As a result of these capabilities, GE-TENG can effectively identify human movements with precision and transmit signals wirelessly, thereby broadening its potential applications. The GE-TENG-based self-powered intelligent sensing system (GSIS) for human-computer interaction demonstrates a wearable intelligent system for controlling the movement of a tank model and a manipulator by integrating the signal acquisition, signal processing and signal output components. The system achieves 100% signal recognition accuracy when combined with covariance matrix feature analysis and Convolutional Neural Network classification algorithms. This research demonstrates the potential of wearable hydrogel-based electronic devices for monitoring human motion, harvesting biomechanical energy and human-computer interaction.

Assessing of Manual Sorting in Municipal Solid Waste: A Study Case in São Paulo City

The dependence of political factors and the lack of integrated municipal waste (MSW) management in metropolitan regions of developing countries turn selective collection and circular economy great challenges to be implemented. This paper presents the characterization of the selective household MSW sent to a manual sorting plant located in the central area of the São Paulo City, aiming to assess the real rejects that are going to sanitary landfill after sorting process. Six sampling campaigns were carried out at the input and output of the sorting process during a year (2016 to 2017). The results show that the materials from the selective collection destined to the plant already arrive with 13.8 % of rejects. In addition, there are about 49.8 % of dry and recycled materials after the sorting process, half of them without commercial value, technical recycling solution available or non-identified. This study shown that, independent of the efficiency and social problems in the totally manual sorting process, which was higher than the local Materials Recovery Facilities, the lack of recycling industries and the presence of dry, but not recyclable materials, as composite packaging, non-identified plastics, Styrofoam and electronics difficult the profitability of this kind of plant.

Respons Mahasiswa terhadap Penerapan Kurikulum Merdeka

Independent Curriculum is a transitional curriculum after the COVID-19 crisis. Independent Curriculum aims to make students have learning independence, think critically, innovatively and creatively. This requires students to be more active (student oriented), able to solve personal and other problems, and able to produce certain projects as the final output of the learning process. Campuses are free to design curricula for learning systems and lecture systems to realize these goals. This research focuses on seeing student responses in the application of the Independent Curriculum in terms of the learning system and lecture system. The research method used is descriptive qualitative with an unstructured interview method. The results showed a positive student response to the implementation of the Independent Curriculum even though there are notes that can be used as reference material for improving curriculum development in the future.

Multi-Task neural network model considering low power output risk for short-term photovoltaic forecasting

As the proportion of installed photovoltaic power generation continues to increase, low output under the influence of large-scale weather systems has an increasingly significant influence on the power grid. There is an urgent need to improve photovoltaic power generation forecasting accuracy and reduce the risk of insufficient output in forecast results. To this end, a multi-task neural network model considering low power output risk (LPOMTN) for short-term photovoltaic forecasting is proposed. First, a numerical weather forecast encoder based on multi-scale CNN-Attention is established, which can extract multi-time scale photovoltaic output characteristics. Then a low-output day prediction module based on parallel CNN decoding and a photovoltaic low-output process prediction module based on the clear-sky model and LSTM were established. The latter uses the prediction results of the former as input, and the two modules perform training modeling in a multi-task learning manner to strengthen the model’s sensitivity to low-output states and improve the accuracy of low-output process predictions. The calculation example results show that LPOMTN has higher average forecasting accuracy for power output processes compared to methods such as XGBoost.

Inter-Departure Time Correlations in PH/G/1 Queues

In non-Markovian tandem queueing networks the output process of one site, which is the input process to the next site, is not renewal. Consequently, the correlation analysis of that output processes is essential when studying such networks. A correlation analysis in the M/G/1 queue has been studied in the literature via derivation of the joint Laplace-Stieltjes transform (LST) of the sum of two consecutive inter-departure times. That LST is obtained by considering all possible cases at departure epochs. However, those epochs are expressed via dependent variables. In this paper, we first extend the analysis to the more general PH/G/1 queue, and investigate various queues, such as E2/G/1 and C2/C2/1. Then, we consider the lag-n correlation, which requires derivation of the joint LST of sum of n + 1 consecutive inter-departure times. Yet, deriving this LST by the common approach becomes impractical for n + 1 ≥ 3, as the number of all possible cases at departure epochs increases significantly. To overcome this obstacle, we derive a corresponding single-parameter LST, which expresses the sum of n + 1 consecutive inter-departure times via the (n + 1)-st departure epoch only. Consequently, the latter LST is expressed via a much fewer number of possible cases, and not less important, as a function of independent variables only, eliminating the need to derive the corresponding joint density. Considering the M/G/1 and the E2/G/1 queues, we demonstrate that the joint LST can be reconstructed directly via the corresponding single-parameter LST when n + 1 = 2. We further conjecture that the multi-parameter joint LST can be reconstructed from the corresponding single-parameter LST in more general queues and for values of n + 1 > 2. The conjecture is validated for various PH/G/1 queues and proved for n + 1 = 3 in the M/G/1 case. The new approach facilitates the calculation of lag-n correlation of the departure process from PH/G/1 queue for n + 1 ≥ 3. Our analysis illuminates the cases when using renewal approximation of the output process provides a proper approximation when studying non-Markovian stochastic networks.

Micro, Small and Medium Enterprises and Sustainable Economic Development in Nigeria

Sustainable economic growth and development may practically be impossible if the indices of growth are not given the required attention that will translate into an enviable sustainability of the fabrics of growth in Nigeria. This has become important given the decline in micro, small and medium enterprise subsector in Nigeria. The essence of this research is thus, to empirically examine the impact of micro, small and medium enterprises on sustainable economic development in Nigeria. The study covered from 2000 to 2020 and annual time series data were sourced from the CBN Statistical Bulletin, World Development Indicator (WDI) and National Bureau of Statistics (NBS). The co-integration and ECM frameworks were used. The results showed that the output of micro, small and medium enterprises has a significant and positive impact on the Per Capita Income (PCY), the exchange rate (EXR) has a significant and positive impact on the micro, small and medium enterprises and Prime Lending Rate (PLR) has a negative and insignificant impact on the micro, small and medium enterprises. Thus, from the results, it was recommended, amongst others, the clustering of micro, small and medium enterprises with similar products, to further improve their output and development process vis-a-vis the growth and development of Nigeria.

Service quality attributes on customers’ perceived value and behavioral intention of district health centres: a moderating effect of health-care subsidy

PurposeThis study aims to explore the influence of health-care service quality on customers’ perceived value, satisfaction, effectiveness and behavioural intention concerning district health centres (DHCs) in Hong Kong. This research also intends to assess customers’ perception of the subsidy scheme and its influence on the relationships amongst the aforementioned constructs.Design/methodology/approachThe convenience and snowball sampling approaches were adopted, and the self-administered questionnaire was sent to 309 customers of DHCs.FindingsService quality attributes in terms of staffing and procedures positively increased customers’ perceived value and staffing, procedures and operations. Physical facilities positively promoted customers’ satisfaction, consequently improving DHCs’ effectiveness and behavioural intention. However, core treatments and services of DHCs did not impact customers’ perceived value and satisfaction. Furthermore, customers receiving subsidies exhibited a more positive perception than those without subsidies.Practical implicationsHealth-care organisations are advised to strategically allocate resources (staffing, facilities and procedures and operations management) to optimise overall performance outcomes. DHC operators could reinforce the core services of DHCs and health-care voucher subsidies to local citizens so as to enhance the effectiveness of DHCs and behavioural intention of customers.Originality/valueThis study integrates the input–process–output approach in measuring the effectiveness of and customers’ behavioural intention towards newly established DHCs.

Pollutant transport model with large time-scale and estimation of land-use export coefficients at a watershed level.

The pollutant transport equilibrium in a watershed can be analyzed on a large time scale, and land-use export coefficients can be calculated directly under certain hydrologic and transport conditions, by ignoring hydrologic and transport processes at small space and time scales on hydrologic response units. In this study, the water environment system of a watershed was deconstructed into three parts (source, source-sink, and runoff transport) to construct a pollutant transportation equilibrium model on a large time scale. A watershed with an annual source-sink accumulation of zero was defined as a completely transported watershed; therefore, we derived a completely transported equilibrium equation. The problem of seeking the land export coefficient was converted into a problem of seeking the optimal solution of linear programming, which can be estimated according to the variation in pollutant output processes. The feasibility of the solution can be analyzed using multi-year stochastic rainfall processes. The model was used to analyze the transport equilibrium of chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) upstream of the monitored cross-sections in a watershed, which covered 3145.66 km2. The land export coefficients were calculated according to the model. The model calculations indicated that the watershed was completely transported during perennial years. The calculated export coefficients of COD, TN, and TP for farmland, primary vegetation, and urban land were within the range of general empirical values. The calculated maximum accumulations of COD, TN, and TP were 0.19 × 107, 0.063 × 107, and 0.049 × 106 kg, respectively, for perennial rainfall. PRACTITIONER POINTS: A completely transported watershed was defined, and a model of pollutant transportation equilibrium with large time-scale was constructed. A problem of seeking the optimal solution of a linear programming was designed to estimate the land export coefficient of COD, TN, and TP. The runoff transport and accumulation processes of COD, TN, and TP in a watershed was analyzed.

Music training is related to late ERP modulation and enhanced performance during Simon task but not Stroop task.

Increasing evidence suggests that music training correlates with better performance in tasks measuring executive function components including inhibitory control, working memory and selective attention. The Stroop and Simon tasks measure responses to congruent and incongruent information reflecting cognitive conflict resolution. However, there are more reports of a music-training advantage in the Simon than the Stroop task. Reports indicate that these tasks may differ in the timing of conflict resolution: the Stroop task might involve early sensory stage conflict resolution, while the Simon task may do so at a later motor output planning stage. We hypothesize that musical experience relates to conflict resolution at the late motor output stage rather than the early sensory stage. Behavioral responses, and event-related potentials (ERP) were measured in participants with varying musical experience during these tasks. It was hypothesized that musical experience correlates with better performance in the Simon but not the Stroop task, reflected in ERP components in the later stage of motor output processing in the Simon task. Participants were classified into high- and low-music training groups based on the Goldsmith Musical Sophistication Index. Electrical brain activity was recorded while they completed visual Stroop and Simon tasks. The high-music training group outperformed the low-music training group on the Simon, but not the Stroop task. Mean amplitude difference (incongruent-congruent trials) was greater for the high-music training group at N100 for midline central (Cz) and posterior (Pz) sites in the Simon task and midline central (Cz) and frontal (Fz) sites in the Stroop task, and at N450 at Cz and Pz in the Simon task. N450 difference peaks occurred earlier in the high-music training group at Pz. Differences between the groups at N100 indicate that music training may be related to better sensory discrimination. These differences were not related to better behavioral performance. Differences in N450 responses between the groups, particularly in regions encompassing the motor and parietal cortices, suggest a role of music training in action selection during response conflict situations. Overall, this supports the hypothesis that music training selectively enhances cognitive conflict resolution during late motor output planning stages.

Biomedical text readability after hypernym substitution with fine-tuned large language models.

The advent of patient access to complex medical information online has highlighted the need for simplification of biomedical text to improve patient understanding and engagement in taking ownership of their health. However, comprehension of biomedical text remains a difficult task due to the need for domain-specific expertise. We aimed to study the simplification of biomedical text via large language models (LLMs) commonly used for general natural language processing tasks involve text comprehension, summarization, generation, and prediction of new text from prompts. Specifically, we finetuned three variants of large language models to perform substitutions of complex words and word phrases in biomedical text with a related hypernym. The output of the text substitution process using LLMs was evaluated by comparing the pre- and post-substitution texts using four readability metrics and two measures of sentence complexity. A sample of 1,000 biomedical definitions in the National Library of Medicine's Unified Medical Language System (UMLS) was processed with three LLM approaches, and each showed an improvement in readability and sentence complexity after hypernym substitution. Readability scores were translated from a pre-processed collegiate reading level to a post-processed US high-school level. Comparison between the three LLMs showed that the GPT-J-6b approach had the best improvement in measures of sentence complexity. This study demonstrates the merit of hypernym substitution to improve readability of complex biomedical text for the public and highlights the use case for fine-tuning open-access large language models for biomedical natural language processing.

Application of Sigma Metrics for Evaluating Analytical Performance of HbA1C Testing on D10- HbA1C Analyzer

In this study, analyzed HbA1C over a period of 6 months. Six Sigma improves the quality of process outputs by analyzing and eliminating the source of defects and reducing variability in manufacturing and business practices. In terms of clinical laboratory, the identification of test with low Sigma values (< 3σ) indicate that actions should be taken to improve analytic quality or the laboratory should use alternate methods and reagents. Our study showed methodologies for HbA1C is of world class performance achieving Sigma value ˃6, to maintain and improve this frequency of QC should be run as rule as per the westguard QC rule. Sigma metrics helps to assess analytical methodologies and augment laboratory performance. It acts as a guide for planning quality control strategy. It can be a self -assessment tool regarding the functioning of clinical laboratory. Using Six Sigma techniques, able to identify problem areas as well as recurring issues that affect the overall quality expectation of laboratory result.
 Keywords: HbA1C, Six Sigma, Six Sigma values

Neutronic Analysis of the Conceptual Molten Uranium Breeder Reactor Using MCNP and SCALE Tools

The Molten Uranium Breeder Reactor (MUBR) is a radically new reactor concept with a mixed-energy spectrum. MUBR is fueled with molten uranium metal in large-diameter fuel tubes and is cooled by circulating molten uranium fuel through a heat exchanger. The reactor has heavy water as moderator, and the reactivity of the reactor is primarily controlled by the voiding effect of the moderator through an innovative control cavity structure design. Because the MUBR design is vastly different from most existing fission reactors, neutronics analysis must be performed for many different combinations of design parameters to identify viable and optimum design configurations. To facilitate the neutronics analysis, a proprietary program called MUBR6gen is being developed to provide a pipeline tool to expedite the process. MUBR6gen employs two well-established neutronics codes, i.e., MCNP and SCALE, to perform standard neutronics calculations for MUBR by automating input preparation and output processing. In addition, MUBR6gen ensures consistency of the MCNP and SCALE inputs and compares the outputs of the two codes to warrant the simulation results. Augmented with MUBR6gen, standard neutronics analysis was carried out on a small-scale MUBR design, which serves as a model problem in the paper. The neutronics performance characteristics of the model reactor were obtained and discussed in a code-to-code pattern. An overall very good agreement between the results of the two neutronics codes was established. Based on the success of the model problem analysis, further neutronics analysis using MUBR6gen was extended for a set of MUBR variant designs. Meaningful and promising fuel cycle analysis results for the 10 different designs were achieved and discussed. These results are used to identify the best MUBR candidates in terms of fuel lifetime and utilization efficiency for future applications.

Co-Pyrolysis of biomass and plastic waste: Process prediction and optimization based on Artificial Intelligence and response optimizer surrogate model

Co-pyrolysis process prediction and optimization has been done through different artificial intelligence processes and response optimizer surrogate model. Neural network and machine learning-based different prediction models have been evaluated to predict the co-pyrolysis output based on the experimental data from literature. While multi-objective optimization has been done through response optimizer. According to prediction models results, CatBoost Regressor (CatB) and Extreme Gradient Boosting (XGB) models’ performance is better than other (seven) models with CatB co-efficient of determinant (R2) 0.92–0.98 while it is 0.91–0.98 for XGB in different scenarios. Therefore, CatB and XGB both models can provide an optimal result for co-pyrolysis prediction. Surrogate-model for multi-objective optimization results concluded that lower portion of biomass waste and higher plastic waste in the feedstock has the optimum co-pyrolysis process output around 550 °C and 60 min of resident time for higher portion of liquid yield with lower gases production. Overall higher portion of the plastic waste in feedstock at high temperature and low resident time promotes more liquid yield. While solid (biochar) production is optimal when there is a higher amount of biomass waste in the feedstock at lower temperature, and longer resident time.