Key Process Variables Research Articles

Laser micro drilling of 316L stainless steel orthopedic implant: A study

It has recently been shown that there is a requirement of inclined micro hole with smooth side walls (without cracks and burr), high aspect ratio, low taper, reduced heat affected zone (HAZ) and thin recast layer on orthopedic implant. So with the aim of improving hole quality characteristics and functionality of implant a multi-objective optimization of laser micro drilling with argon as an assist gas is addressed in this study. A hybrid statistical approach based on grey relational analysis (GRA) and coefficient of variation (COV) is proposed to analyse the experimental data. The key process variables responsible for quality improvement are identified. Four statistical tests are performed to compare the hybrid and GRA approach. Further, it was found that the proposed hybrid is more superior to GRA in terms of computational time and ease of computation. It is also observed that focal point position and diode current have striking effect of hole quality. An important implication of this paper is that thin recast layer, small HAZ width and spatter area were observed, when argon was used as an assist gas.

Phosphorus separation from metallurgical-grade silicon by magnesium alloying and acid leaching

In this paper, the separation of phosphorus from metallurgical-grade silicon was investigated based on an Mg alloying and HCl leaching approach. Experimental results show that P concentration was reduced from initial 15.1 ppmw to 0.2 ppmw with also large extent removal of metallic impurities by two times Mg alloying-leaching purification. The mechanism of enhanced P separation is clarified owing to the strong affinity between Mg and P, which is validated by SIMS elemental mapping. A two-parameter analytical model was developed to predict the P removal degree based on the variables of alloying metal concentration and interaction coefficient between alloying metal and P. The model is validated with experimental results and the interaction coefficient εMginSiP was obtained as −10.8. This approach can be applied to model the removal of impurity which follows Gulliver-Scheil solidification from other binary alloying systems. Furthermore, in order to study the effect of applied alloying-leaching operation times, a model was proposed which establishes the mathematical relationships among key processing variables like initial and target P concentrations, the amount of the alloying metal, and the process operation times.

An investigation on girth friction welding of duplex stainless steel pipes

Girth friction welding is a novel technique to join co-axial linepipes with an external ring (girth) in solid state and without melting of materials. The ring or the girth is rotated between two pipes under an axial force resulting in frictional heat generation and plastic deformation along the pipe-girth interfaces. The rotation of the girth is stopped after a preset displacement of the pipes while the force is maintained to consolidate the deformed material and form the joint along the girth-pipe interfaces. Since the girth is only rotated and the pipeline sections remain stationary, the process simplifies the in situ assembly operations and enables the joining of complex configurations. As the melting of pipes and girth is avoided, the formation of harmful intermetallic compounds and deterioration of toughness in the heat affected zone are minimized. A comprehensive numerical model is reported in the present work to analyse the influence of key process variables on the rate of heat generation, resulting temperature field and thermal cycles, and the nature of material flow along the pipe-girth interfaces in girth friction welding of duplex stainless steel pipes. The computed results of thermal cycles and joint shapes are validated with the corresponding experimentally measured results. The results showed a favorable balance of the ferrite and austenite phases, and very little presence of the harmful sigma phases in the recrystallized weld-zone, which is difficult in fusion welding processes of similar materials.

A Dynamic Active Safe Semi-Supervised Learning Framework for Fault Identification in Labeled Expensive Chemical Processes

A novel active semi-supervised learning framework using unlabeled data is proposed for fault identification in labeled expensive chemical processes. A principal component analysis (PCA) feature selection strategy is first given to calculate the weight of the variables. Secondly, the identification model is trained based on the obtained key process variables. Thirdly, the pseudo label confidence of identification model is dynamically optimized with an historical, current, and future pseudo label confidence mean. To increase the upper limit of the identification model that is self-learning with high entropy process data, active learning is used to identify process data and diagnosis fault causes by ontology. Finally, a PCA-dynamic active safe semi-supervised support vector machine (PCA-DAS4VM) for fault identification in labeled expensive chemical processes is built. The application in the Tennessee Eastman (TE) process shows that this hybrid technology is able to: (i) eliminate chemical process noise and redundant process variables simultaneously, (ii) combine historical pseudo label confidence with future pseudo label confidence to improve the identification accuracy of abnormal working conditions, (iii) efficiently select and diagnose high entropy unlabeled process data, and (iv) fully utilize unlabeled data to enhance the identification performance.

A hybrid back-propagation neural network and intelligent algorithm combined algorithm for optimizing microcellular foaming injection molding process parameters

The microcellular foaming injection molding process can effectively reduce the product weight and facilitate the realization of automobile lightweight, which is greatly affected by process parameters. Warpage of microcellular foaming material will increase, affecting the dimensional stability of the product, unless the process parameters are effectively controlled by process parameters. Warpage of microcellular foaming material has been based on key process variables including mold temperature, melt temperature, coolant temperature, coolant Reynolds number, v/p switch over, initial foaming volume, initial bubble radius and initial gas concentration. In order to reduce warpage of microcellular foaming material, the input and output data obtained from the Finite Element (FE) simulations are used to train BP neural network, GABP neural network and PSOBP neural network as prediction model for the warpage. Comparing the performance of the three methods in prediction error and training time, PSOBP is considered as the best prediction model for the warpage of microcellular foaming material. It has been proved that the prediction model has the ability to predict the warpage of the plastic within an error range of 1 %. The prediction model can be optimized by genetic algorithm to find the best combination of process parameters. The optimized warpage value is 0.7038 mm, which effectively reduces warpage of microcellular foaming material. Finally, finite element simulation and physical tests are carried out to verify the accuracy of the method to optimize microcellular foaming injection molding process parameters.

Investigating the Influence of Process Parameters on the Structural Integrity of an Additively Manufactured Nickel-Based Superalloy

Additive manufacturing (AM) is a novel near net shape manufacturing technology that joins metallic powders layer upon layer in conjunction with 3D model data and as such offers tremendous potential to a wide range of industrial sectors given its ability to produce highly intricate components with very little material wastage. Subsequently, the aerospace industry has become particularly interested in utilising AM as a means of manufacturing nickel-based superalloys for high-temperature applications, such as non-rotating components within gas turbine engines, which are traditionally fabricated through traditional cast and wrought methodologies. As a result of this, a detailed understanding of the influence of key process variables on the structural integrity of the different experimental builds is required. A semi-empirical quantitative approach for melt track analysis has been conducted and the impact on melt track sizing and defect forming mechanisms in the as-built and heat-treated condition is investigated.

Synthesis and Analysis of Nonoxidative Methane Aromatization Strategies

As an abundant, versatile, and cleaner resource, natural gas is underutilized for high‐value chemicals production. Herein, single‐step natural gas conversion technologies that directly produce aromatics and hydrogen from methane are studied. Yet, methane aromatization (MA) is challenging due to low conversion and rapid catalyst deactivation. Accordingly, a reactor model based on thermodynamic equilibrium that allows systematic evaluation of key process variables (e.g., conversion temperature, conversion pressure, and H2 removal) is developed. Then, process synthesis and modeling are utilized to assess the economic viability and identify the key technology gaps for various MA technology alternatives. The results of the analyses indicate that conversion temperature and H2 removal have large impacts on aromatics one‐pass yield and coke formation. The combined conversion conditions, which lead to a minimum 20% aromatics one‐pass yield, are likely required for an economically feasible MA process. In addition, lower conversion temperature (<700 °C) is favorable to maintain coke formation less than 20%. The analysis suggests that further efforts should be devoted to reduce overall capital cost and expand production scale.

Process optimization using response surface methodology for the removal of thorium from aqueous solutions using rice-husk

The adsorptive capability of rice-husk for the sorption of thorium ions from aqueous solutions in batch mode was studied. The key process variables (initial metal ion concentration, initial solution pH and S/L (solid-to-liquid ratio) were optimized for achieving maximum bioremoval efficiency (B%) by employing the Box-Behnken design (33) in response surface methodology (RSM). A quadratic model developed by fitting the experimental data predicted 93% of the responses and estimated the local maximum of B% as >99% for an initial ThIV concentration of 150 g/L, S/L ratio of 5, and an initial pH of 4, and the reported biosorption capacity (qe) is 15.95 mg/g for the same conditions. Freundlich isotherm (R2 = 0.9841) and pseudo-first-order (R2 = 0.9416) kinetic models had the best concurrence with the experimental data in the thorium concentration range used implying the sorption mechanism involves surface biosorption and intraparticle diffusion.

Development and techno-economic analyses of a novel hydrogen production process via chemical looping

In this work, a novel hydrogen production process (Integrated Chemical Looping Water Splitting “ICLWS”) has been developed. The modelled process has been optimised via heat integration between the main process units. The effects of the key process variables (i.e. the oxygen carrier-to-fuel ratio, steam flow rate and discharged gas temperature) on the behaviour of the reducer and oxidiser reactors were investigated. The thermal and exergy efficiencies of the process were studied and compared against a conventional steam-methane reforming (SMR) process. Finally, the economic feasibility of the process was evaluated based on the corresponding CAPEX, OPEX and first-year plant cost per kg of the hydrogen produced. The thermal efficiency of the ICLWS process was improved by 31.1% compared to the baseline (Chemical Looping Water Splitting without heat integration) process. The hydrogen efficiency and the effective efficiencies were also higher by 11.7% and 11.9%, respectively compared to the SMR process. The sensitivity analysis showed that the oxygen carrier–to-methane and -steam ratios enhanced the discharged gas and solid conversions from both the reducer and oxidiser. Unlike for the oxidiser, the temperature of the discharged gas and solids from the reducer had an impact on the gas and solid conversion. The economic evaluation of the process indicated hydrogen production costs of $1.41 and $1.62 per kilogram of hydrogen produced for Fe-based oxygen carriers supported by ZrO2 and MgAl2O4, respectively - 14% and 1.2% lower for the SMR process H2 production costs respectively.

Influence of key operating parameters and bio-fouling on simultaneous desalination of seawater & reduction of organic wastes from petroleum industrial wastewater

ABSTRACT Bio-cathode microbial desalination cells are emerging technology that utilizes bioelectric potential from organic matter (petroleum refinery effluent) to enhance the desalination process along with the activity of microalgae (Scenedesmus abundans). In the present work, the system is assumed to be governed by interdependent key process variables such as salt concentration, anolyte concentration, and external resistance. Three different levels of salt concentration (5, 20 and 35 g L−1), anolyte concentration (250, 500 and 750 mg L−1), and external resistance (100, 560 and 1000 Ω) were analysed and optimized by the statistical and probabilistic tool such as Box Behnken design method and Genetic Algorithm to maximize the desalination performance. The optimized parameters such as salt concentration (35 g L−1), anolyte concentration (500 mg L−1), and external resistance (100 Ω) produced maximum desalination efficiency of 59.6%. The model also distinguished the influence of input parameters on the output responses such as significant responses (Salinity, Total dissolved solids, and Electrical conductivity) and non-significant responses (Chemical oxygen demand removal, power density, and algal growth). The model predicted the desalination efficiency with ±1.25% error. The high impact of bio-fouling was observed in Anion Exchange Membrane on comparison with Cation Exchange Membrane.

Framework for Design Under Uncertainty Including Inherent Safety, Environmental Assessment, and Economic Performance of Chemical Processes

Typically, analysis and design of chemical processes is carried out considering average values of inherently uncertain data that pertain to key design and process variables that shape its performance. In this work, a methodology that includes uncertainty in process safety, an item commonly addressed after the final design has been achieved, along with environmental and economic metrics of the process at the design stage is presented. The approach is based on the use of Monte Carlo simulation techniques to explicitly include uncertainty in the input variables and propagate it through the model. The proposed approach generates a range of performance outcomes through distribution profiles that can be probabilistically characterized, and zones of downside risks and upside opportunities can be identified. A case study that shows the application of the proposed methodology based on the production of ethylene from natural gas was conducted. The results show that although the safest design presented a lower return on investment (ROI), the probability of this scenario to materialize was relatively low, suggesting that a more desirable ROI value for that design would be likely.

Design and Economic Analysis of Liquid CO2 Injection System

In order to curtail the global warming, carbon capture and storage (CCS) has been on the strategies list as one of the potential technologies. Shipping of pressurized CO2 in the liquid phase offers a flexible and economical solution for the transportation of large quantities from the capture site to the storage location. This study presents the design for unloading and injection of liquid CO2 available from the ship-based transport into the reservoir. This study proposed a CO2 injection design in order to explore process improvements compared to the base case design. The proposed design offers less energy consumption, improved operational safety and reduced unit injection cost compared to the base case design. Commercial process simulator, Aspen HYSYS has been used to simulate the CO2 injection system for deciding the key process variables. Literature has widely reported the CO2 shipping conditions of -52 °C and 7 – 8 bara in contrast to the current CO2 shipping practice at -20 °C and 20 bara. The impact of CO2 shipping thermodynamic conditions has been investigated on the injection system in terms of process performance and economics. Finally, a sensitivity analysis has been done in order to investigate the effect of some assumed variables on the process performance.

A process-structure-property model for welding of 9Cr power plant components: The influence of welding process temperatures on in-service cyclic plasticity response

A process-structure-property methodology is presented for welding of 9Cr steels under representative flexible operating conditions for a typical thermal power plant girth-welded pipe. The welding-induced evolution of microstructural variables is represented via (i) a solid-state phase transformation model for martensite-austenite transformation and (ii) empirical equations for prior austenite grain size, martensite lath width, hardness and M23C6 precipitate diameter and area fraction, calibrated from published heat treatment data. The temperature-dependent, physically-based, unified viscoplastic constitutive model, which includes a fatigue damage initiation criterion, is based on dislocation density evolution and is validated against high temperature cyclic plasticity data at a range of relevant temperatures for parent material P91, including combined isotropic-kinematic hardening effects. This model is shown to successfully predict weld-life reduction factor for cross-weld tests. The effects of key welding process variables on the microstructure gradient in the heat-affected zone, and associated thermo-mechanical, cyclic plasticity response are assessed. The inter-critical and fine-grained heat-affected zones are identified as the critical regions, consistent with observed plant experience. Increasing post-weld heat-treatment temperature from 760 °C to 780 °C is predicted to be detrimental due to increased precipitate coarsening. In contrast, increasing preheat and interpass temperature from 350 °C to 400 °C is predicted to be beneficial due to increased hardness in the critical regions.

The Influence of Process Parameters and Build Orientation on the Creep Behaviour of a Laser Powder Bed Fused Ni-based Superalloy for Aerospace Applications.

Additive Layer Manufacturing (ALM) is an innovative net shape manufacturing technology that offers the ability to produce highly intricate components not possible through traditional wrought and cast procedures. Consequently, the aerospace industry is becoming ever more attentive in exploiting such technology for the fabrication of nickel-based superalloys in an attempt to drive further advancements within the holistic gas turbine. Given this, the requirement for the mechanical characterisation of such material is rising in parallel, with limitations in the availability of material processed restricting conventional mechanical testing; particularly with the abundance of process parameters to evaluate. As such, the Small Punch Creep (SPC) test method has been deemed an effective tool to rank the elevated temperature performance of alloys processed through ALM, credited to the small volumes of material utilised in each test and the ability to sample material from discrete locations. In this research, the SPC test will be used to assess the influence of a number of key process variables on the mechanical performance of Laser Powder Bed Fused (LPBF) Ni-based superalloy CM247LC. This will also include an investigation into the influence of build orientation and post-build treatment on creep performance, whilst considering the structural integrity of the different experimental builds.

Design space determination and process optimization in at-scale continuous twin screw wet granulation

At-scale industrial continuous twin screw wet granulation process was analyzed and optimized via design of experiment (DoE). The Box-Behnken method was selected to investigate the influence of key process variables on granule properties and tablet properties. The effects of the key process variables were analyzed. The experimental results have demonstrated that both the granule properties and tablet properties are influenced by the throughput, the screw speed and the screw element. Particularly, the screw element type and the milling process were found having significant effects on tablet tensile strength and disintegration time. The design space (DS) based on volume average granule diameter (Y4 < 800 µm) and tablet disintegration time (Y8 < 4 min) was determined using Monte Carlo simulations. Validation experiments have shown the robustness of the DS. A case study of the DS application is presented. It was demonstrated how to select optimum working conditions of key process parameters based on the DS.

Valorization of Orange Peel Residues via Fluidized Bed Torrefaction: Comparison between Different Bed Materials

ABSTRACTAn experimental study was carried out to evaluate an alternative disposal route for waste orange peels, which includes their upgrade into a solid biofuel and, possibly, the production of valuable liquid bio-chemicals via fluidized bed torrefaction. The influence of the torrefaction temperature (200 and 250°C), the reaction time (5 and 15 min) and the use of different bed materials (Ticino sand and γ-alumina spheres) on the distribution and composition of the main output products (torrefied solids, condensable volatiles and permanent gases) was investigated. Results show that the torrefaction at 250°C transforms orange peels from low-value residue to high-quality energy carrier by increasing their energy content (by a factor of 1.5) and reducing the O/C ratio (up to 67%). The temperature strongly influences the process performance and is the key process variable. Conversely, almost no effect were detected by changing the reaction time and the kind of granular solids on both the distribution and the composition of the main torrefaction products. Although γ-alumina does not exhibit a remarkable catalytic activity in the investigated temperature range, its use instead of sand is recommended as its lower density makes the bed smoother to be fluidized.

Experimental investigation of surface integrity during abrasive edge profiling of nickel-based alloy

Component edge profiling is a novel, complex, labor intensive process that is performed on highly stressed features on aerospace parts such as turbine rotor discs employed in aero-engines. This paper is the first of its kind in this area of research. A novel experimental investigation carried out on surface integrity of Inconel 718 during automated abrasive edge profiling process is presented. Edge profiling tests were conducted using compliant Flexhone™ and Novoflex™globular abrasives on turbine bolt and flange holes to the required aerospace part drawing standard. According to the developed globular brush selection criterion, for a given hole size and edge profile geometry and size, the key process variables that impacted the component life were identified. Nylon impregnated silicon carbide globular brushes (320 grit size) showed a satisfactory abrasive performance between cutting and polishing action on tough class 5 extruded burrs. The optimized abrasive edge profiling parameters were used to manufacture 34 center hole specimens which were tested under low cycle fatigue conditions to confirm the ability of abrasive edge profiling process. Fatigue test results demonstrated that automated abrasive edge profiling does not cause fatigue life deficit compared to manual dressing. Both 2-body and 3-body wear mechanisms were observed. Higher the brushed surface Rv (deepest valley on brushed surface profile), lower the fatigue life of the part.

Dynamical modeling and simulation analysis of a nuclear desalination plant based on the MED-TVC process

Nuclear fission reaction is a clean energy source that can provide thermal power to desalination processes for producing potable water. Due to the inherent safety features of the integral pressurized water reactor (iPWRs) and economic competitiveness of the multi-effect-desalination and thermal-vapor-compression (MED-TVC) process, it is reasonable to interconnect the iPWR with MED-TVC process to develop nuclear desalination plants (NDPs). Dynamic modeling is important for both the design and optimization of the control systems for nuclear desalination plants. In this paper, based on the balance of mass, momentum, energy and salt, a lumped-parameter dynamic model for the NDP composed of an iPWR named NHR-200II and the MED-TVC process is given for the plant control design, which is further deployed as a program on Matlab/Simulink platform. For the modeling verification, the steady points of some key process variables are shown with comparison to the design data, and the open-loop responses under the stepping of both the input seawater flowrate and TVC throat cross-section area are given, which not only show accuracy of this model but also illustrate the coupling between the nuclear steam supply system (NSSS) and MED-TVC modules. Then, a plant control strategy of the NDP based upon NHR-200II reactor and MED-TVC module is newly proposed. Based on the dynamic model, the control verification is finally performed, and the closed-loop responses in the cases of both power maneuvering and MED-TVC module bypassing show the feasibility and satisfactory performance of this control design.

Selective Extraction of Surface-active and Antioxidant Hydrolysates from Yellowfin Tuna Red Meat Protein using Papain by Response Surface Methodology

The present study was focused on the selective extraction of surface-active and antioxidant hydrolysates from yellowfin tuna (Thunnus albacares) red meat based on separate hydrolytic conditions using papain. The effect of key processing variables viz., enzymesubstrate ratio (0.25-1.5 %) and hydrolysis time (30-240 min) under optimized temperature and pH, on the protein recovery, surface-active and antioxidative properties, was determined using Response Surface Methodology (RSM) with a central composite design. Single and combined effects of the variables on the responses were studied by formulating 13 experimental runs. The coefficient of determination (R2) ranged between 0.73 – 0.99 indicating the suitability of the fitted regression models. The optimum hydrolytic conditions to get hydrolysates having superior surface-active properties were enzyme-substrate ratio (E/S) of 0.41 % and 30 minutes hydrolysis time with a desirability of 0.611. Similarly, the optimum conditions to exhibit the highest antioxidative properties with a desirability of 0.932 were: 1.28 % E/S and 240 minutes hydrolysis time.

Data-driven soft sensor for animal cell suspension culture process based on DRVM

In order to solve the problem that key state variables (such as glucose concentration, lactic acid concentration and cell density) in the dynamic process of animal cell suspension culture are difficult to be measured in real time, a data-driven soft sensor based on dynamic relevance vector machine (DRVM) algorithm is proposed. The dominant variables of the soft sensor model are selected according to the mechanisms process. The corrcoef( ) function (belongs to the correlation coefficient command in MATLAB) is used to analyze the correlation among environmental variables, and the auxiliary variables of the soft sensor model are further determined. An improved method on the three edge location algorithm is used to optimize the dynamic weights of the DRVM model. Considering the influence of dynamic transition on soft sensor, the maximum likelihood distribution method under the Bayesian framework is used to train DRVM weight and super parameters, and the dynamic soft sensor model of animal cell suspension culture is established. The proposed method is applied to predict the key state variables in BHK-21 cell suspension culture Process. The experimental results show that compared with the traditional static soft sensing based on RVM, the data-driven soft sensor based on DRVM has higher accuracy, and the rationality and superiority of the method are verified. In order to further realize the real-time online prediction of key state variables, the monitoring interface of the suspension culture process is developed on the LabVIEW virtual instrument platform through its MATLAB Script node, and the data exchange of the DRVM soft sensor for the key state variables of the cell suspension culture process based on MATLAB and monitoring interface is realized.