Process Design Guidelines Research Articles

Hybrid Multi-Criteria Decision Making for Additive or Conventional Process Selection in the Preliminary Design Phase

Additive manufacturing (AM) has become a key topic in the manufacturing industry, challenging conventional techniques. However, AM has its limitations, and understanding its convenience despite established processes remains sometimes difficult, especially in preliminary design phases. This investigation provides a hybrid multi-criteria decision-making method (MCDM) for comparing AM and conventional processes. The MCDM method consists of the Best Worst Method (BWM) for the definition of criteria weights and the Proximity Index Value (PIV) method for the generation of the final ranking. The BWM reduces the number of pairwise comparisons required for the definition of criteria weights, whereas the PIV method minimizes the probability of rank reversal, thereby enhancing the robustness of the results. The methodology was validated through a case study, an aerospace bracket. The candidate processes for the bracket production were CNC machining, high-pressure die casting, and PBF-LB/M. The production of the bracket by AM was found to be the optimal choice for small to medium production batches. Additionally, the study emphasized the significance of material selection, process design guidelines, and production batch in the context of informed process selection, thereby enabling technical professionals without a strong AM background in pursuing conscious decisions.

Monitoring changes in catalyst ink morphology during the primary drying process for polymer electrolyte fuel cells using cryogenic transmission electron microscopy

To control the microstructure formation of a catalyst layer in a polymer electrolyte fuel cell for improving its performance, we observed the real morphology changes of the catalyst ink during the primary drying process using cryogenic transmission electron microscopy. Samples of the thin film catalyst ink, applied onto holey carbon grids, were prepared with different drying times (t = 0, 10, and 30 s). At t = 0, the sample exhibited platinum/carbon agglomerates surrounded by an ionomer thin film in the solvent. At t = 10, the ionomer rods became thicker, as confirmed by high magnification images. At t = 30, light spots were observed in the ionomer/solvent areas, indicating local thinning of the ionomer thin film due to solvent evaporation during the drying process. Moreover, as the drying time increased, the ionomer morphology became more visible with significantly thicker rods. This change in morphology can be attributed to the composition change of the solvent (fraction of water/normal propyl alcohol) in the catalyst ink during drying. Based on the new findings, this study discusses process design guidelines for cathode catalyst layers.

Heat integration of power-to-heat technologies: Case studies on heat recovery systems subject to electrified heating

A systematic design methodology for the electrified supply of heat in process industries is developed to provide process design guidelines for economic and energy-efficient transition from a fossil-fuelled energy system to a renewable-based electricity system. The most appropriate strategy for the implementation of electrified sources for energy systems is proposed in the context of Heat Integration. Design implications related to the introduction of electrified sources for industries are fully investigated for the design of heat recovery systems between conventional fossil-fuelled systems and electrified systems. As the electricity-based heating systems allows more degrees of freedom for the design of the HEN (Heat Exchanger Network) than fossil-fuel-based systems, design complexities of HEN under electrification can be considerably reduced. This is illustrated with a few cases of heat recovery system in which capital cost and design complexities can be minimized by considering the electrified energy supply and their characteristics in an effective and integrated manner. Preliminary techno-economic assessment for electrified energy recovery systems is carried out to understand economic limitation of industrial electrification under current energy prices and its sensitivities.

Predicting pH rise as a control measure for integration of CO2 biomethanisation with anaerobic digestion

In-situ CO2 biomethanisation offers a means to combine biogas upgrading with increased methane productivity, but its potential contribution to power-to-gas is often ignored due to concerns over process stability and control. The research presents an equation derived from fundamental chemical equilibria which predicts the relationship between partial CO2 pressure and digester pH, and allows estimation of the maximum achievable biogas methane content compatible with stable operation. A rapid experimental determination was also developed to support these predictions. The results were validated by long-term experiments using synthetic feedstock with different ammonia concentrations (2 and 3 g N L-1). Further trials carried out using food waste and sewage sludge as substrates showed stable operation at biogas methane contents of 92 and 90% CH4 and pH 8.5 and 7.9, respectively. CO2 biomethanisation was successfully demonstrated in a food waste digester with a total ammoniacal nitrogen of 4.8 g N L-1 with volumetric methane production enhanced by more than 2 times, from 2.29 to 5.01 L CH4 per L digester per day. The predictive approach used is applicable to digesters fed on different feedstocks and to hybrid systems with biomethanisation of both endogenous and exogenous CO2; and offers a basis for both process design guidelines and operational control. The output from the work thus provides engineers, operators and plant designers with a valuable tool for the successful implementation of in-situ biomethanisation in anaerobic digesters.

Investigation of thermoplastic melt flow and dimensionless groups in 3D bioplotting

We investigate the key 3D bioplotting processing parameters, including needle diameter and dispensing pressure, on the shear rates, shear stresses, pressure drops, and swell ratios of extruded miscible polycaprolactone (PCL) blends having a range of viscosities. Assuming simple capillary flow, we construct flow curves and we estimate that the shear stresses inside the needle of the bioplotter range from 2500 to 20,000 Pa and the corresponding shear rates from 2 to 25 s−1, depending upon the viscosity of the blend. We further identify relevant dimensionless numbers that reflect the material rheological properties and processing conditions; these include the capillary number (Ca), Bond number (Bo), Weissenberg number (Wi), and elasticity number (El). At most processing conditions Ca > 1, whereas Bo < 1, suggesting that viscous forces dominated surface forces, except for needle diameters below 0.2 mm, where the flow approached micro-fluidic conditions. While Wi was below 1 at all conditions, El increased significantly with decreasing needle diameter. High El numbers at a needle internal diameter of 0.2 mm were associated with extrudate swell ratios above 2. Based on these results, we define ranges of operation in 3D bioplotting, which can serve as guidelines for process design. Even though this work is specific on the particular bioplotting equipment, the methodology described herein can be applied on any type of micro-extrusion equipment.

Developing a predictive model for nanoimprint lithography using artificial neural networks

Nanoimprint lithography (NIL) is a high-throughput and cost-effective technique for fabricating nanoscale features. However, the fine tuning of NIL process parameters is critical in achieving defect-free imprints. Currently, there exists no unified material and process design guidelines to deal with the complex set of process parameters. In this research, an artificial neural network (ANN) algorithm was developed to predict the imprint quality based on a set of input factors collected from experiments and literature. The prediction accuracy was tested in two stages by the use of three ANN models - General Regression Neural Network (GRNN), Back Propagation Neural Network (BPNN), and Probabilistic Neural Network (PNN). Higher prediction accuracy was observed for GRNN in both stages (1st stage=58.22% and 2nd stage=92.37%). A significant increase in the prediction accuracy was observed in the second stage after including additional input factors and larger data set. The average prediction accuracies for GRNN, BPNN, and PNN in the second stage were reported as 92.37%, 84.79%, and 91.19%, respectively. The prediction of the GRNN model was validated with experimental results confirming the accurate classification of all the outputs. This research lays the foundation for the development of an expert system for nanoimprint lithography.

A pilot study on large-scale microbial enhanced oil recovery (MEOR) in Baolige Oilfield

Industrial application of microbial enhanced oil recovery (MEOR) has been hindered by a lack of large-scale data-based guidelines for process design and operation. In the present work MEOR was investigated in both laboratory and large-scale oilfield studies. Six microbial strains were initially isolated from Baolige Oilfield in China. Laboratory based investigation showed that all six strains were able to decrease the oil viscosity. Two mixtures of strains exhibited greater reduction effects, i.e., 35% and 56%, respectively. The optimal nutrient concentration was found to be 1.0%. The mixtures of strains tested in laboratory core flooding based MEOR also confirmed their greater MEOR performance, i.e., MEOR levels of 9.1% and 13.2%, respectively, compared to that of any single strain ranging from 7.0% % to 8.7%. Using the strain mixture that had been selected under the laboratory based conditions, the pilot field study achieved a significant MEOR: 210,000 tons of crude oil produced over 43 months from 169 production wells. The research results obtained in this work including both laboratory and field studies can be potentially applied in other oilfields with similar geological and physical conditions, for large-scale MEOR process design and operation.

The challenge to determine a company’s process maturity: a case study from the financial services industry

Purpose. Conducting projects to improve a company’s business processes is of utmost importance in all industries and countries. Many companies have installed specific organizational units to develop guidelines for process design, to document and maintain of these processes, and to further increase the processes’ efficiency. Although these enterprises continually work on improving their processes, they often struggle to answer the question on the current status of the maturity of their processes. Therefore, the purpose of this work is to characterize the methodology, applicability, pitfalls and benefits of analyzing the maturity of processes.&#x0D; Design/Method/Approach. This work is based on mixed-methods research recently conducted in a medium-sized German bank.&#x0D; Findings. The paper defined the benefits of measuring the level of maturity of the company’s processes clearly. This work identified the substantial theoretical drawbacks, such as, for example, the lack of considering of process innovation in the extant models of process maturity.&#x0D; Limitations. Naturally, a research limitation is the analysis of a specific company in the financial services sector.&#x0D; Theoretical implications. From a theoretical point of view it is critical to choose the appropriate model out of a variety of available process maturity models. In fact, the selection of the model influences the data, the subsequent interpretation of these data, and the conclusions to be drawn for the company.&#x0D; Originality/Value. The paper is novel as it presents–based on empirical data–the measurement of process maturity including the derivation of implications from both an academic and a practical perspective. In addition, the impact of process maturity on perceived process performance could be shown.&#x0D; Paper type – empirical.

Position deviation of bending point in asymmetric V-die bending process with HSS trapezoid sheet

The prediction of the deviation of bending point in asymmetric V-die bending is extremely needed for practical application. An analytical model and a series of experiments are performed to characterize high strength steel (HSS) sheet metal parts fabricated by asymmetric V-die bending dies and trapezoid sheet. The proposed strategy uses an elementary bending theory to develop a basic model to predict the position deviation of bending point of asymmetric V-die bending process and to test its viability for a trapezoid sheet. Accordingly, a series of experiments obtained good agreement with the calculations. The effects of die radius on deviation in the bending point were experimentally tested to identify the behavior of position deviation in sheet metal bending processes. The experimental results show the important role of contact friction on deviation change; however, its value, i.e., friction coefficient, is always difficult to be determined in calculation. In conclusion, the interactive effects of process parameters are complex and show the important roles of die radius and sheet width on deviation change. The results of this study can be considered when developing process design guidelines for asymmetric processes in HSS sheets.

The Influence of Process Parameters on Non-Conventional Technology in Drawing Cassette Roller Die (Part 1)

Abstract This paper presents a new approach to the influence of the process parameters on non-conventional technology for drawing in cassette roller die. This technology combines two conventional methods of plastic deformation. The rolling, in this method the wire is passed through the two pairs of roller and, the drawing, using the drawing force that exist at the exit of the wire between the rolls. In conclusion there are two forces involved in the process of plastic deformation: drawing force and rolling force. It has been shown that these forces are strongly influenced by variation of certain process parameters such as: the initial diameter of the sample, the elongation coefficient, the contact surface roll/sample, the average pressure of rolling, the friction coefficient and the rollers working radius. The results depicts a helpful insight into the effects of the process parameters in the wire drawing with cassette roller die, thus furnishing the basic guidelines for process design and optimization.

Positional deviation of bending point in asymmetric V-die bending process of HSS sheet: an experimental study

In this study, a series of experiments are performed to characterize the asymmetric V-die bending process for a High-strength steel (HSS) sheet. The main challenge in asymmetric V-die bending is positional deviation of the bending point during bending, given by a magnitude and a direction of deviation of the bending point. The effects of process parameters, such as radii of the punch head and the die shoulder, on the positional deviation of the bending point were experimentally examined to identify those that govern the deviation of the bending point in an asymmetric bending process. This work improves our understanding of deviations of the bending point, and can be used for developing process design guidelines for asymmetric bending of high-strength steel sheets to produce precise, asymmetrically bent parts.

16S rRNA gene high-throughput sequencing data mining of microbial diversity and interactions.

The ubiquitous occurrence of microorganisms gives rise to continuous public concerns regarding their pathogenicity and threats to human environment, as well as potential engineering benefits in biotechnology. The development and wide application of environmental biotechnology, for example in bioenergy production, wastewater treatment, bioremediation, and drinking water disinfection, have been bringing us with both environmental and economic benefits. Strikingly, extensive applications of microscopic and molecular techniques since 1990s have allowed engineers to peep into the microbiology in "black box" of engineered microbial communities in biotechnological processes, providing guidelines for process design and optimization. Recently, revolutionary advances in DNA sequencing technologies and rapidly decreasing costs are altering conventional ways of microbiology and ecology research, as it launches an era of next-generation sequencing (NGS). The principal research burdens are now transforming from traditional labor-intensive wet-lab experiments to dealing with analysis of huge and informative NGS data, which is computationally expensive and bioinformatically challenging. This study discusses state-of-the-art bioinformatics and statistical analyses of 16S ribosomal RNA (rRNA) gene high-throughput sequencing (HTS) data from prevalent NGS platforms to promote its applications in exploring microbial diversity of functional and pathogenic microorganisms, as well as their interactions in biotechnological processes.

Position deviation and springback in V-die bending process with asymmetric dies

Novel finite element analyses and a series of experiments are performed to characterize high strength steel sheet metal parts fabricated by asymmetric V-die bending dies. The proposed strategy uses a finite element analysis of elasticity–plasticity to simulate asymmetric V-die bending process and to test its viability for friction contact processes. Accordingly, a series of experiments obtained good agreement with the numerical simulation. The effects of process parameters (e.g., lubrication (contact friction), material properties, and process geometries) on deviation in the bending point were experimentally tested to identify the main parameters of position deviation in sheet metal bending processes. Moreover, effects of springback phenomenon on bending defects and on the precision of an asymmetric bent component are discussed. The results of this study can be considered when developing process design guidelines for asymmetric processes in high strength steel sheets.

Hysteresis in autothermal methane reforming over Rh catalysts: Bifurcation analysis

Numerical analysis of the hysteresis characteristics of a fixed bed catalytic reactor carrying on autothermal methane reforming over Rh catalyst is presented. A one-dimensional heterogeneous model describing heat and mass transport and simultaneously accounting for methane combustion, steam reforming and dry reforming is adopted. Parametric continuation is performed to compute steady state solution regimes predicted by the model. Bifurcation analysis is illustrated allowing characterizing the influence of feed temperature, gas flow rate, oxygen to methane and water to methane feed ratio values on ignition and extinction. A thorough description of the physical mechanisms determining the evolution of hydrogen selectivity, maximum solid temperature and syngas production within the explored region of parameters space is presented. Particular attention is in this framework devoted to analysing the effect of water addition on reactor bifurcation behavior and performance. The illustrated characterization of hysteretic phenomena provides guidelines for process design, operation and control.

Effect of the primary heat treatment on the bond formation in cold welding of aluminum and steel by cold forging

Cold forging enables the manufacturing of high strength monolithic components. When used as joining technology it can also be extended to an enabler of multi-material product design. The contact conditions at the interface between the components during the cold forging process play a crucial role. The joining of two billets of steel and aluminum is investigated by a finite element analysis with regard to the material distribution and the effect of a primary heat treatment of both materials. These parameters affect the surface enlargement and the contact normal stress which are decisive parameters for the formation of a sound bond between aluminum and the hardly cold weldable steel. The experimental results present the effect of a primary heat treatment on the bond formation. Microscopic analyses of the bond formation allow a deeper insight into the bonding mechanism. Thus, the results lead to process design guidelines for cold welding by cold forging.

Two-Dimensional Self-Limiting Wet Oxidation of Silicon Nanowires: Experiments and Modeling

In this paper, a CMOS compatible silicon nanowire (Si NW) fabrication method on bulk silicon substrate is carried out using the self-limiting oxidation (SLO) to accurately control its size and cross-sectional shape. A predictive model for the 2-D SLO of Si NWs is presented. In this model, both the reduced reaction rate and diffusivity result in the oxidation rate degradation. The orientation dependence and the deformation of silicon core and oxide shell are further discussed here. The modeling results show good agreement with the experimental data within a wide range of oxidation temperatures, oxidation time, and various initial silicon core sizes. This model provides useful process design guidelines for Si nanostructures, especially in controlling the final diameter and cross-sectional shape of Si NWs from the top-down approach.

Assessing the use of activated sludge process design guidelines in wastewater treatment plant projects: A methodology based on global sensitivity analysis

Design inputs (wastewater characteristics, operational settings, effluent requirements or safety factors,…) need to be supplied when using activated sludge process design guidelines (ASPDG) to determine the design outputs (biological reactor volume, the dissolved oxygen demand or the different internal/external recycle flow-rates). The values of the design inputs might have strong effects on the future characteristics of the plant under study. For this reason, there is a need to determine how both design inputs and outputs are linked and how they affect wastewater treatment plant (WWTP) designs. In this paper we assess ASPDG with a methodology based on Monte Carlo (MC) simulations and Global Sensitivity Analysis (GSA). The novelty of this approach relies on working with design input and output ranges instead of single values, identifying the most influential design inputs on the different design outputs and improving the interpretation of the generated results with a set of visualization tools. The variation in these design inputs is attributed to epistemic uncertainty, natural variability as well as operator, owner and regulator decision ranges. Design outputs are calculated by sampling the previously defined input ranges and propagating this variation through the design guideline. Standard regression coefficients (SRC), cluster analysis (CA) and response surfaces (RS) are used to identify/interpret the design inputs that influence the variation on the design outputs the most. The illustrative case study uses the widely recognized Metcalf & Eddy guidelines and presents a didactic design example for an organic carbon (C) and nitrogen (N) removal pre-denitrifying activated sludge plant. Results show that the proposed GSA can satisfactorily decompose the variance of the design outputs (R2 > 0.7): aerobic (VAER) and anoxic (VANOX) volume, air demand (QAIR) and internal recycle flow rate (QINTR). Response surfaces are proposed to facilitate the visualization of how, when and why the design outputs may change when the most influential design inputs are modified. Finally, it is demonstrated that the proposed method is useful for process engineers providing a regional instead of a local picture of a design problem.

Lignin boosts the cellulase performance of a GH-61 enzyme from Sporotrichum thermophile

An enzyme belonging to the glycoside hydrolase family 61 from the thermophilic fungus Sporotrichum thermophile, was functionally expressed in the methylotrophic yeast Pichia pastoris under the transcriptional control of the alcohol oxidase (AOX1) promoter. The enzyme hydrolyzed barley β-glucan, carboxymethyl cellulose, lichenan, wheat arabinoxylan and birchwood xylan showing optimal activity at pH 8 and 65°C. A 2:1 mixture of Celluclast 1.5L and StCel61a was capable of increasing the degree of spruce conversion by 42%. The use of substrates with varying lignin content permitted the detection of a dependence of the enhancing capacity of StCel61a on the radical scavenging capacity of the different lignocellulosics. In the presence of a reductant, StCel61a boosted the efficiency of a mixture of purified cellulases (EGII, CBHI, β-GLUC) by 20%. The synergistic activity exhibited by StCel61a and its dependence on reducing substances provide guidelines for process design towards the production of economically viable bioethanol.

A parametric study on residual stresses and loads in drawing process with idle rolls

A comprehensive study of drawing process with flat idle rolls of round wires is presented through 3D mechanical finite element simulations. An elastic–plastic model is used for the wire material and contact behavior is simulated by a sliding–sticking friction model. The results of numerical simulations are compared with measurements on wires produced with a laboratory equipment. The comparison of drawing load and some geometrical characteristics of experimental samples with numerical model predictions allowed to establish a good correspondence of model with experimental findings, thus validating the numerical model. Residual stress of flat roll drawn wires, pressure distribution on the forming rolls and drawing load are studied. The effects of main process parameters such as initial workpiece diameter, forming rolls diameter and percentage of deformation are investigated. The results present a helpful insight into the process parameters effect in wire drawing with flat idle rolls thus furnishing the basic guidelines for process design and optimization.

Supporting process design for e-business via an integrated process repository

Business process design is an integral part of e-business engineering. Given that e-business models usually involve a wide range of business processes across different business functions with complex activities, events, and documents, process design for e-business is a very challenging task. Although various process reference models (PRMs) have been developed to provide guidelines for process design, research on leveraging multiple PRMs to support process design for e-business has been scant. In this paper, we demonstrate that the diverse process design requirements in e-business are best satisfied by utilizing multiple PRMs via a case study. Then, we propose a collaborative approach grounded in knowledge management theory to integrating multiple process reference models to better support process design in e-business. We equip the integrated process repository with a set of novel features based on Web 2.0 technologies to enhance its utility, efficiency, and quality for process design support. A prototype system is developed and user experiments are conducted to evaluate the system.