Optimal Processing Route Research Articles

A Decade of Solid Oxide Electrolysis Improvements at DTU Energy

Solid oxide electrolysis cells (SOECs) can efficiently convert electrical energy (e.g. surplus wind power) to energy stored in fuels such as hydrogen or other synthetic fuels. Performance and durability of the SOEC has increased orders of magnitudes within the last decade. This paper presents a short review of the R&D work on SOEC single cells conducted at DTU Energy from 2005 to 2015. The SOEC improvements have involved increasing the of the oxygen electrode performance, elimination of impurities in the feed streams, optimization of processing routes, and fuel electrode structure optimization. All together, these improvements have led to a decrease in long-term degradation rate from ~40 %/kh to ~0.4 %/kh for steam electrolysis at -1 A/cm2, while the initial area specific resistance has been decreased from 0.44 Wcm2 to 0.15 Wcm2 at -0.5 A/cm2 and 750 °C.

Optimization strategy for air handling units in spacecraft launching site

Air handling units (AHUs) in spacecraft launching site (SLS) can maintain the spacecraft in specific air conditions before launching, however, the current air processing method is backward and consumes great deal of energy. This paper proposes an optimization strategy for AHUs in SLS to reduce energy consumption. Firstly a reasonable and simplified energy consumption model of the AHU in SLS is built, and the working characteristic of each piece of equipment is analyzed. Secondly minimum energy consumption is taken as optimization target, and corresponding constraints and optimization algorithm are analyzed. By these means, a complete optimization model is built. Next the psychrometric chart is introduced to zone the fresh air working conditions and analyze the air processing routes existing. Fourthly the optimization of each air processing route is implemented and the route demanding minimum energy consumption will be selected as the optimal air processing route. Finally the optimization strategy is applied to analyze the air processing routes for single point and multi-point of typical working conditions, demonstrating its strong ability to save energy.

Nanoscale Microstructure and Chemistry of Cu2ZnSnS4/CdS Interface in Kesterite Cu2ZnSnS4 Solar Cells

Sulfurization with various atmosphere and postheat treatments has been reported for earth abundant kesterite Cu2ZnSnS4 (CZTS) preparation as cost‐effective material for next‐generation solar cells. A full understanding of the nanoscale microstructure and chemistry of CZTS/CdS interface obtained from these different fabrication routes is currently lacking, yet is critical to developing optimal processing routes for high‐performance kesterite solar cells. Here, the first detailed investigation of the interfacial microstructure and chemistry of CdS/Cu2ZnSnS4 heterojunctions is presented. For CZTS obtained from sulfurization in a sulfur‐only atmosphere where highly defective surfaces are present, air annealing followed by etching in the initial stage of chemical bath deposition (CBD) process can effectively eliminate interfacial defects and allow the epitaxial growth of CBD‐CdS, improving the minority lifetime, open circuit voltage (VOC), and fill factor (FF) of the devices, while blocking Cd diffusion and deteriorating short circuit current (Jsc). For CZTS from sulfurization in a combined sulfur and SnS atmosphere where CBD‐CdS can directly epitaxially grow on CZTS and Cd‐diffusion is clearly observed, associated devices show the longest lifetime and the highest efficiency of 8.76%. Epitaxial growth of CdS and Cd diffusion into CZTS are found to be two crucial features minimizing interfacial recombination and achieving high‐efficiency devices. This will not only enhance the understanding of the device structure and physics of kesterite based solar cells, but also provide an effective way for designing other chalcogenide heterojunction solar cells.

Reduction of microkinetic reaction models for reactor optimization exemplified for hydrogen production from methane

Sustainable and efficient processes require optimal design and operating conditions. The determination of optimal process routes, however, is a challenging task. Either the models and underlying chemical reaction rate equations are not able to describe the process in a wide ranges of reaction conditions and thus limit the optimization space, or the models are too complex and numerically challenging to be used in dynamic optimization. To address this problem, in this contribution, a reduction technique for chemical reaction networks is proposed. It focuses on the sensitivity of the reaction kinetic model with respect to the removal of selected reaction steps and evaluates their significance for the prediction of the overall system behavior. The method is demonstrated for a C1 microkinetic model describing methane conversion to syngas on Rh/Al2O3 as catalyst. The original and the reduced microkinetic model show excellent qualitative and quantitative agreement. Subsequently, the reduced kinetic model is used for the optimization of a methane reformer to produce a hydrogen rich gas mixture as feed for polymer electrolyte membrane (PEM) fuel cell applications.

Evaluating Local Primary Dendrite Arm Spacing Characterization Techniques Using Synthetic Directionally Solidified Dendritic Microstructures

Microstructure characterization continues to play an important bridge to understanding why particular processing routes or parameters affect the properties of materials. This statement certainly holds true in the case of directionally solidified dendritic microstructures, where characterizing the primary dendrite arm spacing is vital to developing the process–structure–property relationships that can lead to the design and optimization of processing routes for defined properties. In this work, four series of simulations were used to examine the capability of a few Voronoi-based techniques to capture local microstructure statistics (primary dendrite arm spacing and coordination number) in controlled (synthetically generated) microstructures. These simulations used both cubic and hexagonal microstructures with varying degrees of disorder (noise) to study the effects of length scale, base microstructure, microstructure variability, and technique parameters on the local PDAS distribution, local coordination number distribution, bulk PDAS, and bulk coordination number. The Voronoi tesselation technique with a polygon-side-length criterion correctly characterized the known synthetic microstructures. By systematically studying the different techniques for quantifying local primary dendrite arm spacings, we have evaluated their capability to capture this important microstructure feature in different dendritic microstructures, which can be an important step for experimentally correlating with both processing and properties in single crystal nickel-based superalloys.

Sustainable process synthesis–intensification

Chemical industry is facing global challenges such as the need to find sustainable production processes. Process intensification as part of process synthesis has the potential to find truly innovative and more sustainable solutions. In this paper, a computer-aided, multi-level, multi-scale framework for synthesis, design and intensification of processes, for identifying more sustainable alternatives is presented. Within the framework, a three stage work-flow has been implemented where, in the first “synthesis” stage an optimal processing route is synthesized through a network superstructure optimization approach and related synthesis tools. In the second, “design” stage, the processing route from the first stage is further developed and a base case design is established and analyzed. In the third, “innovation” stage, more sustainable innovative solutions are determined. The application of the framework is illustrated through a case study related to the production of di-methyl carbonate, which is an important bulk chemical due to its multiplicity of uses.

An optimization model of machining process route for low carbon manufacturing

Process route planning selects and defines the whole machining process involved in transforming workblanks into end products. Our research finds that the decisions of processing methods, machines, cutting tools, and sequence of process stages during process route planning have significant impact on carbon emissions of the following manufacture processes. Firstly, a carbon emission and efficiency estimation model of process route is established to achieve the goal of reducing carbon emissions as well as increasing efficiency based on the machining features analyzing. Then, the mathematical model to express process route optimization problem is developed with objectives on minimizing the total carbon emission and total process time. A non-dominated sorting genetic algorithm is introduced to solve this problem, and a simulation study on a machine motor seat is conducted in order to verify the feasibility and practicability of the proposed model. The result of the experiment shows that our model can achieve the goal of reducing emission as well as maintaining system efficiency.

Solving a new multi-objective multi-route flexible flow line problem by multi-objective particle swarm optimization and NSGA-II

Mass production, meeting the increasing demands of the customers is a necessity. Such a production is mainly dependent on a factory manufacturing called flow line production. This paper deals with special type of production by the name of flexible manufacturing system, assuming the presence of multi processors in each station of a multi-station arrangement. The model debated in the paper possesses three objective functions, the first of which attempts to minimize the weighted delays. The second objective function tries to minimize the capital for the purchase of the processors at stations and the third objective function minimizes the capital dedicated to select the optimum processing route of parts. For the validation of the mathematical model, use has been made of NSAGAII and MOPSO approaches.

Effect of Market Price Uncertainties on the Design of Optimal Biorefinery Systems—A Systematic Approach

This paper presents the development of a computer-aided decision support tool for identifying optimal biorefinery concepts for production of biofuels at an early design stage. To this end, a framework that uses a superstructure-based process synthesis approach integrated with uncertainty analysis is used. We demonstrate the application of the tool for generating optimal biorefinery concepts for a lignocellulosic biorefinery. In particular, we highlight the management of various sources of data, the superstructure (integrated thermochemical and biochemical conversion routes) needed to represent the design space, generic but simple models describing the processing tasks, and the formulation and solution of an MINLP problem under deterministic and stochastic conditions to identify the optimal processing route for multiple raw materials and products. Furthermore, we evaluate the impact of market price uncertainties on the optimal solutions and calculate the associated risk to enable informed and risk-aware decisions.

An integrated model for remanufacturing process route decision

The diversity and uncertainty of dimension, damage degree and remaining life characteristics of used parts make the remanufacturing process route decision more complicated. In order to select the optimal remanufacturing process route, an integrated model based on quality function deployment (QFD), fuzzy linear regression and zero–one goal programming is proposed in which machining time (T), machining quality (Q), machining costs (C), resources consumption (R), environmental emissions (E) and engineering characteristics of each remanufacturing process stage are considered as decision objectives and decision variables, respectively. The proposed model provides the means for incorporating not only the relationships between decision objectives and decision variables but also the interactions between decision variables through adopting the QFD principles. The potential use of the proposed model is illustrated through an application.

Multi-objective Machining Process Route Optimization Model for High Efficiency and Low Carbon

The process route rules the whole machining process from blanks to parts. It can directly affect the low carbon production of the mechanical product. To achieve the low-carbon optimization decisions of machining process route, the concept of feature element and machining element is introduced to express the component characteristics, and a multi-objective optimization model is established, which takes the minimum total processing time and the lowest total carbon emissions as the optimization objectives. Then the optimization model is solved based on NSGA-II(Non-dominated sorting genetic algorithm II). An experiment case of a motor seat machining process is performed to verify the feasibility and practicability of the proposed model.

Study of the formation and thermal stability of Mg2Co obtained by mechanical alloying and heat treatment

The microstructural evolution of Mg and Co in a 2:1 atomic ratio was investigated during mechanical alloying and subsequent heat treatments. Microstructural characterization was determined using X-ray diffraction and scanning electron microscopy, while thermal stability was studied by means of differential scanning calorimetry.The results show that mechanical alloying produces amorphization and promotes greater microstructural refinement. Formation of Mg2Co requires an additional heat treatment at temperatures between 679 and 705K, depending on milling time. Additionally, it was determined that the activation energy for Mg2Co crystallization decreases from 206 to 184kJ/mol when the milling time increases from 12 to 36h, respectively. Finally, a combination of the mechanical alloying process and heat treatment phase evolutions was proposed as an optimal processing route in order to obtain the Mg2Co compound.

Process Route Optimization for Machining Center NC Milling Manufacturing

There are some characteristics about CNC milling machining centers, such as the procedures are concentrated, the procedure steps are complex, the machining cost is expensive and so on. A mathematical model was established to optimize the process route for machining center NC milling manufacturing. It wants to make the time of machining a single part is shorter or the cost of machining a single part is lower. A using example was given.

Development of the Smart Metal Casting Process Management System

In this paper, we describe the smart metal casting process management system, in which we applied case-based reasoning to find a near optimal process route on the window environment. The metal casting process is complex and variable depends on a kind of metal casting products. Especially, the metal casting industry has a feature which produces small quantities but produces a lot of different types of metal casting products. And we developed the smart metal casting process management system which could show the process route according to the product cases intelligently using the result of case-based reasoning. The experimental result shows that our metal casting process management system schemes achieves more productivity than manual management schemes.

Integration of Process Routes Planning and Scheduling Based on Network Diagram and Ant Colony Algorithm

The integration method based on network diagram and ant colony algorithm was proposed to realize integration of process routes planning and scheduling. A model of multiple process routes and resource (MPRR) network diagram was built, which applied the network diagram expression and represented the precedence constraint relationship among operations and resource conditions. In the model, the workpiece, operation and machine were joined together as a basic optimization cell to realize concurrent and holistic optimization for process routes planning and scheduling. The combination of MPRR network diagram and characteristics of ant colony algorithm was developed for generating optimal process routes and scheduling project. An example was studied to illustrate the effectiveness of the strategy.

Mechanical alloying and subsequent heat treatment of Ag–Zn powders

Microstructural evolution during mechanical alloying of Ag and Zn, and subsequent heat treatments were investigated. The mechanical alloying was carried out in a SPEX 8000D miller. The microstructural characterization was obtained by X-ray diffraction (XRD), transmission electron microscopy (TEM) and scanning electron microscopy (SEM). The thermal behavior was studied using differential scanning calorimetry (DSC). Based on the results obtained, it can be concluded that at the early stages of milling was possible to detect the ɛ, β, β′, α solid solutions and remaining Zn. Later, the ɛ, β, β′ and Zn phases disappeared while the Zn concentration of the α solid solution was strongly increased. After 7.2 ks of milling, the mechanical alloying process reached a steady state. During this period, both the composition and crystallite size of the α solid solution remained practically unchanged. On the other hand, subsequent heat treatments of milled powders showed that the α solid solution could also be obtained by the combination of mechanical alloying and heat treatment. Finally, the evolution of the microstructure during milling and annealing was combined to propose an optimal processing route in order to obtain a α solid solution.

Investigations of reticulated porous alumina foam ceramics based on different coating techniques with the aid of μCT and statistical characteristics

Alumina foam ceramic filters were prepared due to the “replica technique”. The parameters of the two coating steps were varied to find the optimal processing route to prepare ceramic foam filters with a good homogeneous distribution of the strut thickness. Different additives for the slurry for the impregnation (first coating) step were investigated with the aid of rheological measurements. For the second coating step spraying as well as centrifuging techniques with different processing parameters were studied. The sintered foam structure (strut diameter, pore size and porosity) was characterized with the aid of CT-images and described with an image analysis software and statistical tools.The mechanical properties depend strongly on the achieved structure of the filter and especially on the distribution of the strut thickness in the inner volume side of the filter. A centrifuging technique with a pressure supported dipping as well as optimum processing parameters of the spraying step led to improved mechanical properties as well as homogenous macro-structures.

Research on Network Manufacturing Resources Optimization Deployment

Networked manufacturing environment, making the task decomposition and exist out of line manufacturing cell optimization problem, propose a pre-matching rules and genetic algorithm combined with the resources optimal deployment method. In the process the task decomposition process layer by layer from the pre-match rules for task decomposition process and manufacturing units to form the pre-matching set of manufacturing unit, and when complete the task decomposition process generates the appropriate set of pre-matching manufacturing unit and then use genetic algorithm solution to processing costs, transportation costs, processing time and transportation time to build a multi-objective optimization model; to simplify the calculation, the multi-objective problem by the weight method into a single objective problem; calculated from the manufacturing unit eventually chooses the optimal set of manufacturing cells combination, making the process task global optimum processing route. Finally, exemplify the feasibility of the method.

Modeling the mechanical properties of optimally processed cordierite–mullite–alumina ceramic foams by X-ray computed tomography and finite element analysis

Bulk and cellular cordierite ceramics were prepared from a non-stoichiometric powder consisting of corundum, talc (triclinic), α-quartz, K-feldspar, kaolinite, mullite and a small amount of a glass phase. The optimal sintering processing route was evaluated to obtain good mechanical properties. A high flexural strength of 120MPa and a Young’s modulus of 99GPa were achieved. The ceramic foams were fabricated by impregnation of polymer preforms with the optimized stock suspension. The mechanical properties of ceramic foams were studied by impulse excitation and compression tests. The Gibson–Ashby model predicted the ceramic foam’s effective modulus and its elastic limit strength well, as measured experimentally. In addition, the actual three-dimensional (3-D) structure obtained from X-ray computed tomography (CT) coupled with the finite element method (FEM) was used to calculate the Young’s modulus and the elasticity limit of the ceramic foam; however, this did not produce aby better agreement between the calculated values and the experimental results. The discrepancy between the Gibson–Ashby model and FEM could probably be attributed to the accuracy and small volume of representative reconstructed 3-D cellular structure. Taking account of the effect of the internal hollow structure on the stress localization in the ceramic struts, the CT–FE modeling provides a good measure of the adaptability and predictability of actual ceramic foam structures for realistic damage modeling.

On development of hypoeutectic aluminium–silicon powder metallurgy alloy

Powder metallurgy allows for the rapid, automated and efficient production of many different types of automotive components. However, a drawback is the limited selection of readily available light alloy blends. Owing to the wide spread use of aluminium–silicon casting alloys for existing components it is logical to develop aluminium–silicon PM options. Therefore, an experimental hypoeutectic aluminium–silicon alloy was chosen for study and an optimum processing route developed. Tests were performed to determine the green strength and density as a function of compaction pressure. Sintering conditions were optimised based on sintered density, hardness and dimensional changes. Metallography, differential scanning calorimetry and energy dispersive X-ray spectroscopy analysis provided insight into post-sinter furnace cooling and heat treatment parameters. An appropriate T6 heat treatment was developed and samples were tested in tension. The alloy was able to achieve a high sintered density approaching 98% and a yield strength of 232 MPa under the T6 condition.