The recent revolution in shale gas has presented opportunities for distributed manufacturing of key commodity chemicals, such as methanol, from methane. However, the conventional methane‐to‐methanol ...process is energy intensive which negatively affects the profitability and sustainability. We report an intensified process configuration that is both economically attractive and environmentally sustainable. This flowsheet is systematically discovered using the building block‐based representation and optimization methodology. The new process configuration utilizes membrane‐assisted reactive separations and can have as much as 190% higher total annual profit compared to a conventional configuration. Additionally, it has 57% less CO2‐equivalent greenhouse gas emission. Such drastic improvement highlights the advantages of building block‐based computer‐aided process intensification method.
Energy efficient process planning for CNC machining Newman, S.T.; Nassehi, A.; Imani-Asrai, R. ...
CIRP journal of manufacturing science and technology,
2012, 2012-1-00, Letnik:
5, Številka:
2
Journal Article
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Machining is one of the major activities in manufacturing industries and is responsible for a significant portion of the total consumed energy in this sector. Performing machining processes with ...better energy efficiency will, therefore, significantly reduce the total industrial consumption of energy. In this paper, a framework is presented to validate the introduction of energy consumption in the objectives of process planning for CNC machining. The state of the art in process planning and energy consumption in manufacturing research is utilised as a basis for the framework. A mathematical representation of the logic used is presented followed by two sets of experiments on energy consumption in machining to validate the logic. It is shown that energy consumption can be added to multi-criteria process planning systems as a valid objective and the discussion on using resource models for energy consumption estimation concludes the paper. These experiments represent a part test procedure machining proposal for the new environmental machine standard ISO 14955 Part 3.
Automated machining feature recognition, a sub-discipline of solid modeling, has been an active research area for last three decades and is a critical component in digital manufacturing thread for ...detecting manufacturing information from computer aided design (CAD) models. In this paper, a novel framework using Deep 3D Convolutional Neural Networks (3D-CNNs) termed FeatureNet to learn machining features from CAD models of mechanical parts is presented. FeatureNet learns the distribution of complex manufacturing feature shapes across a large 3D model dataset and discovers distinguishing features that help in recognition process automatically. To train FeatureNet, a large-scale mechanical part datasets of 3D CAD models with labeled machining features is automatically constructed. The proposed framework can recognize manufacturing features from the low-level geometric data such as voxels with a very high accuracy. The developed framework can also recognize planar intersecting features in the 3D CAD models. Extensive numerical experiments show that FeatureNet enables significant improvements over the state-of-the-arts manufacturing feature detection techniques. The developed data-driven framework can easily be extended to identify a large variety of machining features leading to a sound foundation for real-time computer aided process planning (CAPP) systems.
•A novel deep 3D CNN framework to learn machining features from CAD models.•A large-scale labeled manufacturing features dataset with 3D CAD models.•Significant improvements over the state-of-the-arts manufacturing feature detection.
Abstract
The recent revolution in shale gas has presented opportunities for distributed manufacturing of key commodity chemicals, such as methanol, from methane. However, the conventional ...methane‐to‐methanol process is energy intensive which negatively affects the profitability and sustainability. We report an intensified process configuration that is both economically attractive and environmentally sustainable. This flowsheet is systematically discovered using the building block‐based representation and optimization methodology. The new process configuration utilizes membrane‐assisted reactive separations and can have as much as 190% higher total annual profit compared to a conventional configuration. Additionally, it has 57% less CO
2
‐equivalent greenhouse gas emission. Such drastic improvement highlights the advantages of building block‐based computer‐aided process intensification method.
Process simulation based on physical models often faces computational problems with respect to convergence, especially if the underlying flowsheets are complex. The use of data‐driven surrogate ...models connected to flowsheets promises to overcome these challenges. Using the steam methane reforming process, this paper presents the development of surrogate models – artificial neural networks – for the key units of the process that are subsequently connected to form the entire flowsheet. The accuracy of the individual surrogate models is analyzed based on the test error; the accuracy of the flowsheet is evaluated by a benchmark process simulation performed in Aspen Plus®. Therefore, the predicted key variables, here outlet temperatures and compositions, are compared to the benchmark. It is shown that their maximum error is below the typical measurement error. The comparison of the accuracy of the surrogate‐based flowsheet simulation with the Aspen Plus® simulation proves to match very well, as long as the training ranges of the underlying surrogate models are not violated. The promising results of this paper pave the way for future work, such as the optimization of process parameters or superstructure optimization.
This paper presents the development of surrogate models for the key units of the SMR process that are subsequently connected to form the entire flowsheet. It is shown that the results obtained by the surrogate‐based model match very well with the Aspen Plus® simulation results. This paves the way for future work, such as process optimization.
Simultaneous approaches for dynamic optimization problems are surveyed and a number of emerging topics are explored. Also known as direct transcription, this approach has a number of advantages over ...competing dynamic optimization methods. Moreover, a number of industrial applications have recently been reported on challenging real-world applications. This study provides background information, summarizes the underlying concepts and properties of this approach, discusses recent advances in the treatment of discrete decisions and, finally, illustrates the approach with two process case studies.
We review the literature about shop scheduling problems in manufacturing systems, revealing the concepts and methodologies that most impact the usage of scheduling theory in manufacturing ...environments. We focus our attention on the job shop and flow shop problems and their variants. We emphasize the interactions between the scheduling functions and manufacturing paradigms such as Industry 4.0, Computer Integrated Manufacturing, Computer‐Aided Process Planning, Advanced Planning and Scheduling, and Integrated Process Planning and Scheduling. We describe the main components and characteristics of the scheduling ecosystem, and we discuss how the scheduling interacts with the components that make it up and how it is affected by them. The metadata collected from the digital libraries on which the review was based (ScienceDirect, Scopus, and Elsevier) made it possible to characterize the historical evolution of the main concepts of the scheduling ecosystem in terms of scientific publications and research trends in the period 2000–2020.
Waste valorization is one of the key aspects of cleaner production, there are a large number of residues such as crustacean exoskeletons which can be potentially used for obtaining valuable products. ...A comprehensive analysis involving both exergy and environmental aspects of chitosan production from shrimp shell wastes is presented. Mass and energy balances, needed to assess the presented process, were obtained by using Aspen Plus ® software. It was obtained a chitosan production rate of 12,152 t/y from a fed flow of 57,000 t/y of shrimp exoskeleton. Process simulation allowed estimation of environmental performance by applying the Waste Reduction Algorithm (WAR). The results revealed that the process consumes potential environmental impacts (PEI), obtaining negative values for these variables (in terms of generation rates) in all cases. Besides, the exergy analysis reported a global exergy efficiency of 4.58%, obtaining that astaxanthin removal stage reached the highest irreversibilities and exergy of wastes (707,699.57 MJ/h and 707,738.09 MJ/h). The sensitivity analysis showed that the efficiency of depigmentation stage can be increased by 92%, applying process improvements. This means an increase of the global exergy efficiency by 58.93%.
The Rankine cycle serves as a crucial technical tool for waste-heat recovery. Matching heat sources and working fluids presents a challenging problem among the extensive research on the process ...design and selection of working fluids for the Rankine cycle. This study introduces a process design methodology for the Rankine cycle based on heat matching. It can optimize the performance between heat sources and working fluids without the need to preset the configuration for the cycle. The objective is to ultimately derive the most efficient configuration and operating conditions for the Rankine cycle. This method relies on establishing a matching relationship between temperature and heat diagram curves of both heat sources and working fluids. The relationship can be described by a simple linear programming problem, serving as the fundamental model for the proposed method. Demonstration of the feasibility and accuracy of this method involved computing the performance of a subcritical Rankine cycle for waste heat recovery in an ammonia-diesel dual-fuel engine. Three different working fluids were considered: ammonia, R123, and R245fa. Ammonia exhibited superior performance with an overall system efficiency improvement of 5.18 %, surpassing R123 (5.01 %) and R245fa (5.00 %). The competitiveness of the ammonia Rankine cycle has been established, resulting in a nearly zero-carbon-emission power system potentially achieving an overall efficiency of 53.23 %. The process design method based on heat matching notably reduces the modeling efforts required for the Rankine cycle. This reduction provides substantial support for engineering design and facilitates wider applications of the Rankine cycle.
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•Establish a concise Process design method for Rankine cycle based on heat matching.•Optimize heat matching using linear programming.•Zero carbon ammonia Rankine cycle is more suitable for ammonia engines.
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