Model-based Optimisation of Mixed Refrigerant LNG Processes

Natural gas liquefaction processes are energy and cost intensive. This thesis pursues the optimisation of propane precooled mixed refrigerant (C3MR) processes considering variations in upstream gas well conditions, in order to maximise gas well life. Four objective functions were selected for the design optimisation of the C3MR and dual mixed refrigerant (DMR) processes: 1) total shaft work (W), 2) total capital investment, 3) total annualised cost, and 4) total capital cost of both compressors and main cryogenic heat exchanger (MCHE). Optimisation results show that objective function 4 is more suitable than other objective functions for reducing both W and UA (MCHE design parameter). This leads to 15% reduction in specific power for C3MR and 27% for DMR, while achieving lower UA values relative to baseline. The operation optimisation of the C3MR process and its split propane version (C3MR-SP) was performed using four objective functions: 1) total shaft work, 2-3) two different exergy efficiency expressions, and 4) operating expenditure (OPEX). Objective function 3 results in the lowest specific shaft work 1469 MJ/tonne-LNG. For C3MR-SP, however, the lowest specific shaft work is found to be under objective function 1. A comparison of optimisation results across literature studies is impractical due to dissimilar process conditions, feed gas conditions, product quality, and equipment size. A sensitivity analysis highlights the effect of feed gas conditions on performance of the C3MR. For instance, as LNG production decreases from 3 MTPA to 2.4 MTPA over time, the specific OPEX increases from $128/tonne-LNG to$154/tonne-LNG. A subsequent study was conducted focusing on energy benefits of two configurations: integrating natural gas liquids (NGL) recovery unit with C3MR. An integrated NGL recovery within C3MR shows a 0.74% increase in energy consumption as methane concentration of the feed gas decreases, however a frontend NGL recovery unit only has a 0.18% decrease

A generalized computationally efficient inverse characterization approach combining direct inversion solution initialization with gradient-based optimization

A computationally efficient gradient-based optimization approach for inverse material characterization from incomplete system response measurements that can utilize a generally applicable parameterization (e.g., finite element-type parameterization) is presented and evaluated. The key to this inverse characterization algorithm is the use of a direct inversion strategy with Gappy proper orthogonal decomposition (POD) response field estimation to initialize the inverse solution estimate prior to gradient-based optimization. Gappy POD is used to estimate the complete (i.e., all components over the entire spatial domain) system response field from incomplete (e.g., partial spatial distribution) measurements obtained from some type of system testing along with some amount of a priori information regarding the potential distribution of the unknown material property. The estimated complete system response is used within a physics-based direct inversion procedure with a finite element-type parameterization to estimate the spatial distribution of the desired unknown material property with minimal computational expense. Then, this estimated spatial distribution of the unknown material property is used to initialize a gradient-based optimization approach, which uses the adjoint method for computationally efficient gradient calculations, to produce the final estimate of the material property distribution. The three-step [(1) Gappy POD, (2) direct inversion, and (3) gradient-based optimization] inverse characterization approach is evaluated through simulated test problems based on the characterization of elastic modulus distributions with localized variations (e.g., inclusions) within simple structures. Overall, this inverse characterization approach is shown to efficiently and consistently provide accurate inverse characterization estimates for material property distributions from incomplete response field measurements. Moreover, the solution procedure is shown to be capable of extrapolating significantly beyond the initial assumptions regarding the potential nature of the unknown material property distribution

Text-to-Video: a Two-stage Framework for Zero-shot Identity-agnostic Talking-head Generation

The advent of ChatGPT has introduced innovative methods for information gathering and analysis. However, the information provided by ChatGPT is limited to text, and the visualization of this information remains constrained. Previous research has explored zero-shot text-to-video (TTV) approaches to transform text into videos. However, these methods lacked control over the identity of the generated audio, i.e., not identity-agnostic, hindering their effectiveness. To address this limitation, we propose a novel two-stage framework for person-agnostic video cloning, specifically focusing on TTV generation. In the first stage, we leverage pretrained zero-shot models to achieve text-to-speech (TTS) conversion. In the second stage, an audio-driven talking head generation method is employed to produce compelling videos privided the audio generated in the first stage. This paper presents a comparative analysis of different TTS and audio-driven talking head generation methods, identifying the most promising approach for future research and development. Some audio and videos samples can be found in the following link: https://github.com/ZhichaoWang970201/Text-to-Video/tree/main.Comment: 6 page

A Scheduling Strategy of Mobile Parcel Lockers for the Last Mile Delivery Problem

In the form of unattended Collection-and-Delivery Points (CDP), the fixed parcel lockers can save courier miles and improve the delivery efficiency. However, due to the fixed location and combination, the fixed parcel locker cannot accommodate the change of demands effectively. In this paper, an approach to supplementing fixed lockers by mobile parcel lockers to meet the demands of the last mile delivery has been proposed. With the goal of minimizing the operating cost, the location and route optimization problems of mobile parcel lockers are integrated into a non-linear integer programming model. An embedded GA has been developed to optimally determine the locations of distribution points, the number of mobile parcel lockers needed by each distribution point and the schedules and routes of mobile parcel lockers, simultaneously. Finally, a numerical example is given to compare the optimization results of the schemes with and without the aggregation problem. The results show that the scheme with the aggregation problem can greatly save the delivery time. However, for the scheme without the aggregation problem, time windows are more continuous, so it saves the number of vehicles

Computational Inverse Solution Strategies for Characterization of Localized Variations of Material Properties in Solids and Structures

Computational inverse characterization approaches that combine computational physical modeling and nonlinear optimization minimizing the difference between measurements from experimental testing and the responses from the computational model are uniquely well-suited for quantitative characterization of structures and systems for a variety of engineering applications. Potential applications that are suited for computational inverse characterization range from damage identification of civil structures to elastography of biological tissue. However, certain challenges, primarily relating to accuracy, efficiency, and stability, come along with any computational inverse characterization approach. As such, proper application-specific formulation of the inverse problem, including parameterization of the field to be inversely determined and selection/implementation of the optimization approach are critical to ensuring an accurate solution can be estimated with minimal (i.e. practically applicable) computational expense. The present work investigates strategies to optimally utilize the available measurement data in combination with a priori information about the nature of the unknown properties to maximize the efficiency and accuracy of the solution procedure for applications in inverse characterization of localized material property variations. First, a strategy using multi-objective optimization for inverse characterization of material loss (i.e., cracks or erosion) in structural components is presented. For this first component, the assumption is made that sufficient a priori information is available to restrict the parameterization of the unknown field to a known number and shape of material loss regions (i.e., the inverse problem is only required to identify size and location of these regions). Since this type of parameterization would typically be relatively compact (i.e., low number of parameters), the inverse problem is well suited for non-gradient-based optimization approaches, which can provide accuracy through global search capabilities. The multi-objective inverse solution approach shown divides the available measurement data into multiple competing objectives for the optimization process (rather than the typical single objective for all measurement data) and uses a stochastic multi-objective optimization technique to identify a Pareto front of potential solutions, and then select one "best" inverse solution estimate. Through simulated test problems of damage characterization, the multi-objective optimization approach is shown to provide increased solution estimate diversity during the search process, which results in a substantial improvement in the capabilities to traverse the optimization search space to minimize the measurement error and produce accurate damage size and location estimates in comparison with analogous single objective optimization approaches. An extension of this multi-objective approach is then presented that addresses problems for which the quantity of localized changes in properties is unknown. Thus, a self-evolving parameterization algorithm is presented that utilizes the substantial diversity in the Pareto front of potential solutions provided by the multi-objective optimization approach to build up the parameterization iteratively with an ad hoc clustering algorithm, and thereby determine the quantity, size, and location of localized changes in properties with minimal computational expense. Similarly as before, through simulated test problems based on characterization of damage within plates, the solution strategy with self-evolving parameterization is shown to provide an accurate and efficient process for the solution of inverse characterization of localized property changes. For the second half of the present work, a substantial change in the inverse problem assumptions is made, in that the nature (i.e., shape) of the property variation is no longer assumed to be known as precisely a priori Thus, a more general (e.g., mesh-based) parameterization of the unknown field is needed, which would typically come at a cost of significantly increased computational expense and/or loss of solution uniqueness. To balance the generalization of the approach and still utilize some amount of the knowledge that the solution is localized in nature, while maintaining efficiency, a hybrid compact-generalized parameterization approach is presented. The initial incarnation of this hybrid approach combines a machine learning data reconstruction strategy known as gappy proper orthogonal decomposition (POD) with a least-squares direct inversion approach to estimate material stiffness distribution in solids (i.e., to solve elastography problems). The direct inversion approach uses a generalized mesh-based parameterization of the unknown field, but full-field response measurements (i.e., measurements everywhere in the solid) are required, which are not available for most practical inverse characterization problems. Therefore, the gappy POD technique first identifies the pattern of potential response fields of the solid through a collection of a priori forward numerical analyses of the solid response with a specified compact parameterization and a corresponding collection of arbitrarily generated parameter sets. Once the pattern is identified, the gappy POD technique is able to use the available partial-field measurement data to estimate the full-field response of the solid to be used by the direct inversion. Thus, the computational cost of the inverse characterization is negligible once the gappy POD process has been completed. Through simulated test problems relating to characterization of inclusions in solids, the direct inversion approach with gappy POD is shown to provide highly efficient and relatively accurate inverse characterization results for the prediction of Young's modulus distributions from partial-field measurement data. This direct inversion approach is further validated through an example problem regarding characterization of the layered stiffness properties of an engineered vessel from ultrasound measurements. Lastly, an extension of this hybrid approach is presented that uses the characterization results provided by the previous direct inversion approach as the initial estimate for a gradient-based optimization process to further refine/improve the inverse solution estimate. In addition, the adjoint method is used to calculate the gradient for the optimization process with minimal computational expense to maintain the overall computational efficiency of the inverse solution process. Again, through simulated test problems based on the characterization of localized, but arbitrarily shaped, inclusions within solids, the three-step (gappy POD - direct inversion - gradient-based optimization) inverse characterization approach is shown to efficiently provide accurate and relatively unique inverse characterization estimates for various types of inclusions regardless of inclusion geometry and quantity

Stochastic simulation and statistical inference platform for visualization and estimation of transcriptional kinetics

Recent advances in single-molecule fluorescent imaging have enabled quantitative measurements of transcription at a single gene copy, yet an accurate understanding of transcriptional kinetics is still lacking due to the difficulty of solving detailed biophysical models. Here we introduce a stochastic simulation and statistical inference platform for modeling detailed transcriptional kinetics in prokaryotic systems, which has not been solved analytically. The model includes stochastic two-state gene activation, mRNA synthesis initiation and stepwise elongation, release to the cytoplasm, and stepwise co-transcriptional degradation. Using the Gillespie algorithm, the platform simulates nascent and mature mRNA kinetics of a single gene copy and predicts fluorescent signals measurable by time-lapse single-cell mRNA imaging, for different experimental conditions. To approach the inverse problem of estimating the kinetic parameters of the model from experimental data, we develop a heuristic optimization method based on the genetic algorithm and the empirical distribution of mRNA generated by simulation. As a demonstration, we show that the optimization algorithm can successfully recover the transcriptional kinetics of simulated and experimental gene expression data. The platform is available as a MATLAB software package at https://data.caltech.edu/records/1287