Key Process Variables Research Articles

Process variables predicting changes in adolescent alcohol consumption and mental health symptoms following personality-targeted interventions

ObjectiveThis study aims to identify key process variables that are associated with changes in alcohol consumption and mental health symptoms over 12months following personality-targeted interventions in youth. Method154 high-risk youth (aged 12–13years) in 7 Montreal high schools were identified using the Substance Use Risk Profile Scale and participated in personality-matched interventions. Preliminary process variables were identified using a combination of psychotherapy process variables and youth-generated (qualitative) feedback immediately post-intervention. ResultsLearning, skill development and a positive group experience were key to positive behavioural change. Youth-generated feedback independently accounted for 12–25% of the variance in the change in alcohol use and mental health symptoms over 12months. Changes in cognitive distortions and self-esteem accounted for somewhat less of the variance in alcohol use (0–9%), but a moderate-to-large portion of the variance in changes in mental health symptoms (up to 44%). ConclusionsThe study findings highlight candidate process variables relevant to future implementations of this program that might inform change processes relevant to brief interventions with youth more generally. This study suggests that youth experiences can indicate proximal measures of program efficacy, and has implications for the dissemination of this brief intervention program.Clinical Trial registered on www.ClinicalTrials.gov, “Does Delaying Adolescent Substance Use Lead to Improved Cognitive Function and Reduce Risk for Addiction”, study NCT01655615.

Integrated predictive maintenance strategy for manufacturing systems by combining quality control and mission reliability analysis

Predictive maintenance (PdM) is an effective means to eliminate potential failures, ensure stable equipment operation and improve the mission reliability of manufacturing systems and the quality of products, which is the premise of intelligent manufacturing. Therefore, an integrated PdM strategy considering product quality level and mission reliability state is proposed regarding the intelligent manufacturing philosophy of ‘prediction and manufacturing’. First, the key process variables are identified and integrated into the evaluation of the equipment degradation state. Second, the quality deviation index is defined to describe the quality of the product quantitatively according to the co-effect of manufacturing system component reliability and product quality in the quality–reliability chain. Third, to achieve changeable production task demands, mission reliability is defined to characterise the equipment production states comprehensively. The optimal integrated PdM strategy, which combines quality control and mission reliability analysis, is obtained by minimising the total cost. Finally, a case study on decision-making with the integrated PdM strategy for a cylinder head manufacturing system is presented to validate the effectiveness of the proposed method. The final results shows that proposed method achieves approximately 26.02 and 20.54% cost improvement over periodic preventive maintenance and conventional condition-based maintenance respectively.

Dynamical Models of the Camber and the Lateral Position in Flat Rolling

In flat rolling, the lateral position of the product in the rolling mill and the camber (curvature of the product centerline seen in top view) are key process variables. We explore how their evolution can be analytically modeled based on nonlinear geometric relations, material derivatives, balance equations, constitutive equations for the material flow in the roll gap, and a change of coordinates to obtain a time-free formulation. Based on example problems, we verify the developed novel model and further illustrate the mechanisms behind it. Finally, a literature review on models in this field reveals that there is not yet a consensus on the correct analytical model of the evolution of the camber in flat rolling. The literature review shows that most published models are special cases of the model developed in this paper.

Gum ghatti mediated, one pot green synthesis of optimized gold nanoparticles: Investigation of process-variables impact using Box-Behnken based statistical design

Due to unique inherent catalytic characteristics of different size, shape and surface functionalized gold nanoparticles, their potential applications, are being explored in various fields such as drug delivery, biosensor, diagnosis and theranostics. However conventional process for synthesis of these metallic nanoparticles utilizes toxic reagents as reducing agents, additional capping agent for stability as well as surface functionalization for drug delivery purposes. Hence, in this work suitability of gum Ghatti for reducing, capping and surface functionalization during the synthesis of stable Gold nanoparticles were duly explored. Role and impact of key process variables i.e. volume of chloroauric acid solution, gum solution and temperature at their respective three different levels, as well as mechanism of formation of optimized gold nanoparticles were also investigated using Box- Behnken design. These novel synthesized optimized Gold nanoparticles were further characterized by UV spectrophotometer for its surface plasmon resonance (SPR) at around ∼530nm, dynamic light scattering (DLS) for its hydrodynamic size (112.5nm), PDI (0.222) and zeta potential (−21.3mV) while, transmission electron microscopy (TEM) further revealed surface geometry of these nanoparticles being spherical in shape.

Variational Bayesian Gaussian Mixture Regression for Soft Sensing Key Variables in Non-Gaussian Industrial Processes

In this brief, a variational Bayesian Gaussian mixture regression (VBGMR) method is developed for soft sensing key quality-related variables in a non-Gaussian industrial process. Traditional Gaussian mixture regression (GMR) is based on Gaussian mixture model (GMM) and can be easily stuck into the issue of model selection since the GMM model generally requires a large amount of local components so as to achieve desirable predicting performances. However, such assignation can be computationally extensive and may also result in some numerical issues since only a few components are positively adopted. In this brief, a fully Bayesian modeling method is proposed for GMR-based soft sensor development. Conjugate Bayesian prior is defined for each empirical parameter, while a Dirichlet process prior is further defined on the mixture component. The full Bayesian GMR is first validated on a numerical case study and then applied to the industrial hydrogen manufacturing units, both compared with GMR. The results demonstrate feasibility and reliability of the new soft sensor and show that VBGMR generally outperforms GMR with the requirement of only a few activated components.

Thermodynamic modeling and uncertainty quantification of CO2-loaded aqueous MEA solutions

The accurate characterization of the thermodynamic models of a reactive solvent-based CO2 capture system is essential for adequately capturing system behavior in a process model. Moreover, uncertainty in these models can significantly affect simulation results, although it is often neglected. With this incentive, a model of thermodynamic properties, including vapor-liquid equilibrium (VLE), enthalpy, and solution chemistry, has been developed using a rigorous methodology for the aqueous monoethanolamine (MEA) system as a baseline. The final thermodynamic framework consists of both a deterministic and stochastic model. The deterministic model is developed by regressing parameters of the e-NRTL activity coefficient model in Aspen Plus® to VLE, heat capacity, and heat of absorption data while downselecting the model’s large parameter space through use of information-theoretic criteria. The stochastic model is developed through an uncertainty quantification (UQ) procedure, using the results of the deterministic regression to determine a prior parameter distribution. This prior distribution and the experimental VLE data are used to derive a posterior distribution through Bayesian inference, which is used to represent the final stochastic model. A reaction model in which the kinetics are written in terms of the reaction equilibrium constants, to ensure consistency with the thermodynamics model, is developed. These new thermodynamic and reaction models are incorporated into an existing MEA system process model from the open literature, and the prior and posterior parameter distributions are propagated through the models. This provides valuable insight into the extent to which uncertainty in thermodynamic models affects key process variables, including CO2 capture efficiency and energy requirement for solvent regeneration.

Design, dynamic modeling, and control of a multistage CO2 compression system

This paper presents a comprehensive model of a post-combustion CO2 compression system that uses a multistage centrifugal compressor train to take the CO2-rich gas from the regenerator of a CO2 capture system and compress it to the supercritical conditions required for pipeline transport. The compression system model includes inter-stage coolers, flash vessels, glycol tower for water removal, and the CO2 inventory of the associated system. Both inline and integral gear compressors are designed using dimensionless numbers and appropriate design constraints. The steady-state integral gear compressor model is validated against industrial compressor performance data provided by a commercial CO2 compressor manufacturer. Its performance is also compared to the performance of an inline compressor model using a typical feed stream from a post-combustion CO2 capture process. Performance curves using dimensionless exit flow coefficient and isentropic head coefficient are used for simulating off-design performance of the integral gear compressor. To avoid surge during operation, a controller has been designed. Transients of key process variables in response to various disturbances have been studied.

Optimization of critical quality attributes in continuous twin-screw wet granulation via design space validated with pilot scale experimental data

In this study, the influence of key process variables (screw speed, throughput and liquid to solid (L/S) ratio) of a continuous twin screw wet granulation (TSWG) was investigated using a central composite face-centered (CCF) experimental design method. Regression models were developed to predict the process responses (motor torque, granule residence time), granule properties (size distribution, volume average diameter, yield, relative width, flowability) and tablet properties (tensile strength). The effects of the three key process variables were analyzed via contour and interaction plots. The experimental results have demonstrated that all the process responses, granule properties and tablet properties are influenced by changing the screw speed, throughput and L/S ratio. The TSWG process was optimized to produce granules with specific volume average diameter of 150μm and the yield of 95% based on the developed regression models. A design space (DS) was built based on volume average granule diameter between 90 and 200μm and the granule yield larger than 75% with a failure probability analysis using Monte Carlo simulations. Validation experiments successfully validated the robustness and accuracy of the DS generated using the CCF experimental design in optimizing a continuous TSWG process.

A new Self-Organizing Extreme Learning Machine soft sensor model and its applications in complicated chemical processes

The control of product quality of complex chemical processes strictly depends on the measure of the key process variables. However, the online measure device is extremely expensive, and these devices are hard to protect. Meanwhile, there is a delay for these online measure devices. Therefore, the soft sensor technology plays a vital role in measuring the key process variables. Extreme Learning Machine (ELM) is an efficient and simple single layer feed-forward neural networks (SLFNs) to building an exact soft sensor model. However, unsuitable selected hidden nodes and random parameters will greatly affect the performance of the ELM. Therefore, this paper proposes a novel Self-Organizing Extreme Learning Machine (SOELM) algorithm constructed by the biological neuron-glia interaction principle to solve the issue of the ELM. Firstly, the weights between input layer nodes and the CNS are tuned iteratively by the Hebbian learning rule. Then the network structure is adjusted self-organizing by Mutual Information (MI) among different structures of networks. Secondly, the weights between the CNS and output layer nodes are obtained by the ELM. The experimental results based on different UCI data sets prove that the SOELM has a better generalization capability and stability than that of the ELM. Moreover, our proposed method is developed as a soft sensor model for accurately predicting the key variables of the Purified Terephthalic Acid (PTA) process.

Programming a Robot for Conformance Grinding of Complex Shapes by Capturing the Tacit Knowledge of a Skilled Operator

This paper describes a novel methodology to reduce the effort in automating manual surface finishing processes by bridging the knowledge transfer gap of the manual operator's skills to a robot program. Key process variables (KPVs), i.e., contact force, tool path, and feed rate, of the manual operator performing the task are captured with a “sensorized” hand-held belt grinder, while the changes to the work-piece geometry is captured using a 3-D scanner. The entire manual tool-path strategy is segmented into its primitives or primary strategies before programming an equivalent robotic tool-path and strategy. The manual tool-path primitives are imported into computer-aided-manufacturing software where boundary splines are created to generate the robotic tool-paths. An analytical material removal rate (MRR) model is used to scale the extracted manual KPVs such that the parameters can be executed by the robotic platform, while still maintaining an equivalent material removal profile. In the first experimental trial with the designed robotic finishing strategy using this approach, the work-piece could be finished to within 0.7 mm of the desired shape.

Identification of key features using topological data analysis for accurate prediction of manufacturing system outputs

Topological data analysis (TDA) has emerged as one of the most promising approaches to extract insights from high-dimensional data of varying types such as images, point clouds, and meshes, in an unsupervised manner. To the best of our knowledge, here, we provide the first successful application of TDA in the manufacturing systems domain. We apply a widely used TDA method, known as the Mapper algorithm, on two benchmark data sets for chemical process yield prediction and semiconductor wafer fault detection, respectively. The algorithm yields topological networks that capture the intrinsic clusters and connections among the clusters present in the data sets, which are difficult to detect using traditional methods. We select key process variables or features that impact the system outcomes by analyzing the network shapes. We then use predictive models to evaluate the impact of the selected features. Results show that the models achieve at least the same level of high prediction accuracy as with all the process variables, thereby, providing a way to carry out process monitoring and control in a more cost-effective manner.

Parametric investigation of polymethacrylate monolith synthesis and stability via thermogravimetric characterisation

AbstractPolymethacrylate monoliths are synthetic adsorbents with macroporous and mesoporous interconnected channels that can be engineered to target the hydrodynamic features of a wide range of molecular species. However, rigorous study into the effect of synthesis conditions on their thermal stability is limited. This work attempts to characterise the influence of key synthesis process variables on the stability of polymethacrylate monoliths using thermogravimetric analysis at a heating rate of 10 °C/min. Experimental results showed that the thermal stability of polymethacrylate monoliths increased with decreasing polymerisation temperature from 85 to 65 °C. Increasing the total porogen (P) to monomer (M) ratio increased the thermal stability of the monolith by >62% and >50% for P40/M60–P60/M40 and P60/M40–P80/M20, respectively. The impact of the initiator concentration, monomer variation, biporogen ratio, washing and activation of the monoliths was investigated. Nuclear magnetic resonance analyses conducted confirmed the hydrolysis of epoxy moieties on the monolith. © 2017 Curtin University of Technology and John Wiley & Sons, Ltd.

Dynamic Failure Analysis of Process Systems Using Principal Component Analysis and Bayesian Network

Modern industrial processes are highly instrumented with more frequent recording of data. This provides abundant data for safety analysis; however, these data resources have not been well used. This paper presents an integrated dynamic failure prediction analysis approach using principal component analysis (PCA) and the Bayesian network (BN). The key process variables that contribute the most to process performance variations are detected with PCA, while the Bayesian network is adopted to model the interactions among these variables to detect faults and predict the time-dependent probability of system failure. The proposed integrated approach uses big data analysis. The structure of BN is learned using past historical data. The developed BN is used to detect faults and estimate system failure risk. The risk is updated subsequently as new process information is collected. The updated risk is used as a decision-making parameter. The proposed approach is validated through a case of a crude oil distillation u...

Open Loop and Closed Loop Performance of Solid Oxide Fuel Cell Turbine Hybrid Systems During Fuel Composition Changes

The dynamic behavior of a solid oxide fuel cell gas turbine hybrid system (SOFC/GT) from both open and closed loop transients in response to sudden changes in fuel composition was experimentally investigated. A pilot-scale (200–700 kW) hybrid facility available at the U.S. Department of Energy, National Energy Technology Laboratory was used to perform the experiments using a combination of numerical models and actual equipment. In the open loop configuration, the turbine speed was driven by the thermal effluent fed into the gas turbine system, where the thermal effluent was determined by the feedforward fuel cell control system. However, in the closed loop configuration, a load-based speed control system was used to maintain the turbine speed constant at 40,500 rpm by adjusting the load on the turbine, in addition to the implementation of the fuel cell system control. The open loop transient response showed that the impacts of fuel composition changes on key process variables, such as fuel cell thermal effluent, turbine speed, and cathode feed stream conditions, in the SOFC/GT systems were propagated over the course of the test, except for the cathode inlet temperature. The trajectories of the aforementioned variables are discussed in this paper to better understand the resulting mitigation/propagation behaviors. This will help lead to the development of novel control strategies to mitigate the negative impacts experienced during fuel composition transients of SOFC/GT systems.

Optimized Process and Tool Design for GaN Chemical Mechanical Planarization

In this paper, we present a systematic approach to the gallium nitride (GaN) chemical mechanical planarization (CMP) process through evaluating the effect of crystallographic orientation, slurry chemistry and process variables on the removal rate and surface quality responses. A new CMP process and a complementary tool set-up are introduced to enhance GaN material removal rates. The key process variables are studied to set them at an optimal level, while a new slurry feed methodology is introduced in addition to a new tool set up to enable high material removal rates and acceptable surface quality through close control of the process chemistry. It is shown that the optimized settings can significantly improve the material removal rates as compared to the literature findings while simultaneously enabling a more sustainable process and potential removal selectivity against silica.

Shear forming of 304L stainless steel – microstructural aspects

Shear forming is an incremental cold forming process. It transforms 2D plates into 3D structures commonly consisting of conical geometry. Roller(s) push the blank onto a cone-shaped mandrel, resulting in a decrease of the initial thickness. The shear forming process has diverse advantages, such as improved material utilisation, enhanced product characteristics, good surface finish, consistent geometric control and reduced production costs. Shear forming has potential applications in a wide range of conical geometries used within advanced aerospace structures, which are currently manufactured from bulk forgings with high associated machining costs. Research findings related to shear forming have been published over the past two decades, however, important remaining questions have still to be answered, with this paper addressing one such gap associated with the material deformation mechanism. Several studies have demonstrated the impact of key process variables on the final geometry and surface roughness, such as the feeds, roller nose radius and mandrel/roller offset. Although the material outputs are essential, as they link directly with the mechanical properties of the final components, the microstructure and texture of the material after shear forming have rarely been studied. Achieving a greater understanding in this area could reduce the reliance upon mechanical testing to validate the process and ease the exploitation route of the technology into advanced aerospace applications. Firstly, this paper presents the principle of shear forming and its related terminology. Then, a brief overview of the shear forming process including its history and origin is given. The areas of focus are a selection of the main variables encountered within this process which could impact the final properties. The generation of local stresses due to deviations from the sine law, the angle variations, and forces required during the forming operation are also considered. This is explored in the context of forming 304L stainless steel plates.

Optimisation of Weld Overlay Cladding Parameters Using Full-Factorial Design of Experiment

Weld Overlay Cladding (WOC) shares the same scientific principals as conventional welding where there are multiple governing factors that control the process and outcome. The present work employs a Design of Experiment (DoE) approach to optimising process parameters for cladding a nickel superalloy onto low alloy steel with the aim to improve productivity and quality. The arc current, the clad metal heating current were identified as the key process variables for this stage of experimentation. A full-factorial 4-by-2 test was carried out to identify the optimal levels. Results showed that there is a mild positive trend between the height of individual strings of beads and both variables. However no relationship was established with the depth of penetration, nor with the height of single or double layer stacks. The optimal level of the variables was therefore chosen to be the one that has the highest productivity rate as there were no significant differences. Further experimentation has been planned and described in this paper.

A combined Taguchi and response surface approach to improve electret charge in corona-charged fibrous electrets

In this work, an attempt was made to model, analyze, and optimize corona charging process for production of fibrous electrets using a combined Taguchi and response surface approach. The key process variables determining the magnitude and duration of electret charge were screened out using Taguchi approach and optimized using response surface approach. The simultaneous optimization of magnitude as well as duration of electret charge led to an initial surface potential of 1.6 kV and half-decay time of 11.18 min.

Reaction of nitrous oxide with methane to synthesis gas: A thermodynamic and catalytic study

The aim of the present study is to explore the coherence of thermodynamic equilibrium predictions with the actual catalytic reaction of CH4 with N2O, particularly at higher CH4 conversions. For this purpose, key process variables, such as temperature (300°C–550°C) and a molar feed ratio (N2O/CH4= 1, 3, and 5), were altered to establish the conditions for maximized H2 yield. The experimental study was conducted over the Co-ZSM-5 catalyst in a fixed bed tubular reactor and then compared with the thermodynamic equilibrium compositions, where the equilibrium composition was calculated via total Gibbs free energy minimization method.The results suggest that molar feed ratio plays an important role in the overall reaction products distribution. Generally for N2O conversions, and irrespective of N2O/CH4 feed ratio, the thermodynamic predictions coincide with experimental data obtained at approximately 475°C–550°C, indicating that the reactions are kinetically limited at lower range of temperatures. For example, theoretical calculations show that the H2 yield is zero in presence of excess N2O (N2O/CH4= 5). However over a Co-ZSM-5 catalyst, and with a same molar feed ratio (N2O/CH4) of 5, the H2 yield is initially 10% at 425°C, while above 450°C it drops to zero. Furthermore, H2 yield steadily increases with temperature and with the level of CH4 conversion for reactions limited by N2O concentration in a reactant feed. The maximum attainable (from thermodynamic calculations and at a feed ratio of N2O/CH4= 3) H2 yield at 550°C is 38%, whereas at same temperature and over Co-ZSM-5, the experimentally observed yield is about 19%.Carbon deposition on Co-ZSM-5 at lower temperatures and CH4 conversion (less than 50%) was also observed. At higher temperatures and levels of CH4 conversion (above 90%), the deposited carbon is suggested to react with N2O to form CO2.

Experimental investigation on rice husk ash as cement replacement on concrete production

The emission of CO2 has increased due to cement manufacturing and improper disposal of rice hush ash (RHA) leads to air pollution and land fill problem. To mitigate these issues, the RHA has been used as cement additive in concrete making. A Taguchi L27 fractional-factorial matrix was designed to assess the individual effects of key process variables like RHA loading, pozzolanicity, curing time, bulk density and RHA size. From the results, mechanical strength increased with decreasing RHA size and 20wt% RHA replacement is optimum for 15 and 60min grounded sample. The morphology of RHA are also discussed.