A case study of process facility optimization using discrete event simulation and genetic algorithm

Optimization problems such as resource allocation, job-shop scheduling, equipment utilization and process scheduling occur in a broad range of processing industries. This paper presents modeling, simulation and optimization of a port facility such that effective operational management is obtained. A GA base approach has been integrated with the port system model to optimize its operation. A case study of bulk material port handling systems is considered

Modeling of Complex Parts for Industrial WaterJet Cleaning

Industrial high-pressure waterjet cleaning is common to many industries. The modeling in this paper functions inside a collaborative robotic framework for high mix, low volume processes where human robot collaboration is beneficial. Automation of pressure washing is desirable for economic and ergonomic reasons. An automated cleaning system needs path simulation and analysis to give the operator insight into the predicted cleaning performance of the system. In this paper, ablation, the removal of a substrate coating by waterjet, is modeled for robotic cleaning operations. The model is designed to work with complex parts often found in spray cleaning operations, namely parts containing hidden portions, holes, or concavities. Experimentation is used to validate and calibrate the ablation model to yield accurate evaluations for how well every feature of a part is cleaned based on the cumulative effect of water affecting the part surface. The ablation model will provide the foundation for optimizing process parameters for robotic waterjet cleaning

Applications of Dynamic Modeling in Crushing Plants

Modeling is a tool to describe phenomena in a simplified way, and the models can then be used to simulate these phenomena. Models of equipment used in the mining and aggregate industries can be used for process simulations of the processes in those industries to improve the operations. To study processes and the operation of processes, time dynamic models are a great tool. This thesis focuses on applications of time dynamic modeling in crushing plants. The time dynamic models predict the output of the equipment as a function of time. The work presented within this thesis focuses on three areas; Unit modeling, process modeling, and control modeling.Unit modeling refers to developing models of single processing units, which could be a comminution unit, classification unit, or materials handling unit. The new models presented in this thesis are for jaw crushers, high pressure grinding rolls (HPGR), and storage units (e.g., bin, silo, or stockpile). The developed models are based on the fundamental insight of the physics that happens within the unit. The validity of the models is aimed to be broad and cover many operating points and uses. The models are intended for high fidelity process simulation applications.Process modeling refers to the modeling of many interconnected units, and the modeling presented in this thesis has been done with both high-fidelity unit models and with simplified models. Both high fidelity and simple simulations are demonstrated within the thesis. The simpler models are used to try new concepts of plant design or control and study plant robustness or ability to handle variations. Meanwhile, the high-fidelity models can be used to study topics such as particle size distribution, debottlenecking and specific control issues.Control modeling refers to developing controller models to control plants like those modeled within the process modeling section. Optimal control, such as model predictive control (MPC), relies on models to steer processes optimally relative to some objective. The models within those controllers have been discussed in this thesis. Additionally, being able to move between the various fidelity domains of models is beneficial for this application. In this thesis, multiple new models and methods are presented, along with how they can be applied within the minerals processing and aggregate industry, ultimately improving the efficiency and performance of the industries

Modeling and Performance Analysis of Manufacturing Systems in Footwear Industry

This study deals with modeling and performance analysis of footwear manufacturing using arena simulation modeling software. It was investigated that modeling and simulation is a potential tool for modeling and analysis of manufacturing assembly lines like footwear manufacturing because it allows the researcher to experiment with different variables and controls the manufacturing process without affecting the real production system. In this study Arena simulation software is employed to model and measure performance of existing manufacturing systems of footwear. A footwear assembly plant producing a moccasin model shoe in Ethiopia with a total number of 19 major parts to be assembled on two consecutive assembly lines (stitching and lasting) were selected for the model. Furthermore, 39 and 37 activities were identified for stitching and lasting production line respectively. For each activity, 15 numbers of observations have taken using stopwatch. All the collected data are statistically analyzed using arena input analyzer for statistical significance and determination of expressions to be used in simulation modeling. A standard validated simulation model was developed and run for 41 replications. The result shows that the stitching assembly line is operating with a line balance efficiency of 58.7% and lasting assembly line 67.6%. In the course of action, about four major problems were identified and solved with five proposed scenarios of which the best scenario results in improvement of assembly line balance efficiency of 93.5 and 86.3% for stitching and lasting respectively. This Arena Simulation Model has considered the production resources like machineries, employees and processing time; activity precedence relationships; and production methods in developing and testing scenarios. It can be applied to other complex manufacturing industries wishing to analyze and improve the performance of the production systems.Keywords: Modeling Simulation Performance Analysis Footwear Manufacturin

Dynamic torsional modeling and analysis of a fluid mixer

Mixers and agitators are used in a variety of processing industries. Each application has its own uniqueness requiring a high degree of customization in process design and mechanical design. Many of the processing and mechanical performance characteristics of mixers are derived from torque cell and tachometer measurements usually located between the motor and speed reducer. This thesis deals with the development of a dynamic modeling and analysis procedure to simulate the torsional response of mixers. This procedure will allow for the characterization of the torsional response at any point within the system, as well as relate the response as observed at the measurement location on full scale tests to any point of interest within the system. Various modeling options were developed for each of the mixing subsystems and compared to determine which configurations more accurately describe the system torsional dynamics. The developed modeling options were simulated using Simulink and MATLAB. For torsional frequency verification of the simulation model, a finite element model was constructed, analyzed, and compared to the simulation model. Also, the results of a full scale test were obtained and compared to the simulation model. Recommendations for usage, further study, and model development are also discussed

Integrated Model-Centric Decision Support System for Process Industries

To bring the advances in modeling, simulation and optimization environments (MSOEs), open-software architectures, and information technology closer to process industries, novel mechanisms and advanced software tools must be devised to simplify the definition of complex model-based problems. Synergistic interactions between complementary model-based software tools must be refined to unlock the potential of model-centric technologies in industries. This dissertation presents the conceptual definition of a single and consistent framework for integrated process decision support (IMCPSS) to facilitate the realistic formulation of related model-based engineering problems. Through the integration of data management, simulation, parameter estimation, data reconciliation, and optimization methods, this framework seeks to extend the viability of model-centric technologies within the industrial workplace. The main contribution is the conceptual definition and implementation of mechanisms to ease the formulation of large-scale data-driven/model-based problems: data model definitions (DMDs), problem formulation objects (PFOs) and process data objects (PDOs). These mechanisms allow the definition of problems in terms of physical variables; to embed plant data seamlessly into model-based problems; and to permit data transfer, re-usability, and synergy among different activities. A second contribution is the design and implementation of the problem definition environment (PDE). The PDE is a robust object-oriented software component that coordinates the problem formulation and the interaction between activities by means of a user-friendly interface. The PDE administers information contained in DMD and coordinates the creation of PFOs and PIFs. Last, this dissertation contributes a systematic integration of data pre-processing and conditioning techniques and MSOEs. The proposed process data management system (pDMS) implements such methodologies. All required manipulations are supervised by the PDE, which represents an important advantage when dealing with high volumes of data. The IMCPSS responds to the need for software tools centered in process engineers for which the complexity of using current modeling environments is a barrier for broader application of model-based activities. Consequently, the IMCPSS represents a valuable tool for process industries, as the facilitation of problem formulation is translated into incorporation of plant data in less error-prone manner, maximization of time dedicated to the analysis of processes, and exploitation of synergy among activities based on process models

Probabilistic Physics-integrated Neural Differentiable Modeling for Isothermal Chemical Vapor Infiltration Process

Chemical vapor infiltration (CVI) is a widely adopted manufacturing technique used in producing carbon-carbon and carbon-silicon carbide composites. These materials are especially valued in the aerospace and automotive industries for their robust strength and lightweight characteristics. The densification process during CVI critically influences the final performance, quality, and consistency of these composite materials. Experimentally optimizing the CVI processes is challenging due to long experimental time and large optimization space. To address these challenges, this work takes a modeling-centric approach. Due to the complexities and limited experimental data of the isothermal CVI densification process, we have developed a data-driven predictive model using the physics-integrated neural differentiable (PiNDiff) modeling framework. An uncertainty quantification feature has been embedded within the PiNDiff method, bolstering the model's reliability and robustness. Through comprehensive numerical experiments involving both synthetic and real-world manufacturing data, the proposed method showcases its capability in modeling densification during the CVI process. This research highlights the potential of the PiNDiff framework as an instrumental tool for advancing our understanding, simulation, and optimization of the CVI manufacturing process, particularly when faced with sparse data and an incomplete description of the underlying physics

On-line Process Physics Tests via Lyapunov-based Economic Model Predictive Control and Simulation-Based Testing of Image-Based Process Control

Next-generation manufacturing involves increasing use of automation and data to enhance process efficiency. An important question for the chemical process industries, as new process systems (e.g., intensified processes) and new data modalities (e.g., images) are integrated with traditional plant automation concepts, will be how to best evaluate alternative strategies for data-driven modeling and synthesizing process data. Two methods which could be used to aid in this are those which aid in testing data-based techniques on-line, and those which enable various data-based techniques to be assessed in simulation. In this work, we discuss two techniques in this domain which can be applied in the context of chemical process control, along with their benefits and limitations. The first is a method for testing data-driven modeling strategies on-line by postulating the experimental conditions which could reveal if a model is correct, and then attempting to collect data which could help to reveal this. The second strategy is a framework for testing image-based control algorithms via simulating both the generation of the images as well as the impacts of control on the resulting systems