Computer-aided Process Research Articles

Human-in-the-loop Multi-objective Bayesian Optimization for Directed Energy Deposition with in-situ monitoring

Directed Energy Deposition (DED) is a free-form metal additive manufacturing process characterized as toolless, flexible, and energy-efficient compared to traditional processes. However, it is a complex system with a highly dynamic nature that presents challenges for modeling and optimization due to its multiphysics and multiscale characteristics. Additionally, multiple factors such as different machine setups and materials require extensive testing through single-track depositions, which can be time and resource-intensive. Single-track experiments are the foundation for establishing optimal initial parameters and comprehensively characterizing bead geometry, ensuring the accuracy and efficiency of computer-aided design and process quality validation. We digitized a DED setup using the Robot Operating System (ROS 2) and employed a thermal camera for real-time monitoring and evaluation to streamline the experimentation process. With the laser power and velocity as inputs, we optimized the dimensions and stability of the melt pool and evaluated different objective functions and approaches using a Response Surface Model (RSM). The three-objective approach achieved better rewards in all iterations and, when implemented in a real setup, allowed to reduce the number of experiments and shorten setup time. Our approach can minimize waste, increase the quality and reliability of DED, and enhance and simplify human-process interaction by leveraging the collaboration between human knowledge and model predictions.

Design and 3D simulation of weft-knitted jacquard plush fabrics

Abstract Weft-knitted jacquard plush fabrics are widely used and have a wide variety of varieties, but the design and production process is complicated, time-consuming, and labor-intensive. To reduce the time and cost of fabric sampling and production, a method for designing and simulating these fabrics is proposed based on an analysis of their structural characteristics. Following the design principles of weft-knitted jacquard plush fabrics, mathematical models for pattern design and structural design were established sequentially, leading to the generation of a mathematical model for the knitting diagram. Arrays for raw material editing and threading editing were created to set various parameters. Second, loop models were established based on plush loop shapes, including a forming loop model, a U-shaped loop model, a Henkel plush loop model, a “8”-shaped plush loop model, and a O-shaped plush loop model. Finally, the effects of lighting and number of segments were investigated in THREE.Tube Geometry on the simulation’s effectiveness and speed. By comparing actual fabric images with simulation images, the feasibility of the design and simulation method was verified. This resulted in a computer-aided design process for weft-knitted jacquard plush fabrics, providing an effective method for their design and simulation. The proposed method holds significant theoretical and practical values.

A method and tool for Sustainability-driven Computer-Aided Process Planning with user-tuneable optimization

This paper introduces the methodology framework, and a cloud-based Sustainability-driven Computer-Aided Process Planning (s-CAPP) tool aimed at optimizing efficiency and sustainability aspects of manufacturing operations at shopfloor level, considering a user-tailored optimization goal function. Geometric data extracted from 3D models of the parts to be produced are coupled with process parameters and resource consumption data from the manufacturing equipment in order to analyze the metrics for productivity and sustainability accordingly. By leveraging comprehensive data resources in manufacturing, s-CAPP allows companies to balance their production plans between productivity and sustainability through a user-friendly interface. Its application in a furniture manufacturing case study demonstrates its potential for process planning by prioritizing between productivity or sustainability, showcasing promising results. The presented s-CAPP implementation assists the production managers in taking decisions regarding sustainability and productivity in the production of batches through the prompting of intuitive visual results, impacting on both economic and environmental sides. Keywords: sustainability-driven manufacturing, process planning, cloud-based computing, furniture manufacturing, process simulation, data-driven manufacturing, feature extraction

An automatic generation approach of process model based on feature knowledge and geometric modeling

Traditional process planning applies knowledge to convert design information within the part model into process information. This information is then displayed by manually drawing two-dimensional (2D) process cards from multiple views, illustrating the geometric design and the process details. However, this method has revealed several issues, including poor efficiency, information misinterpretation, and the potential for manual drawing errors. This traditional approach hampers the integration of manufacturing information across engineering software. It exacerbates the digital divide between computer-aided design (CAD), computer-aided process planning (CAPP), and computer-aided manufacturing (CAM). To address these issues, this study proposes an automated approach for creating three-dimensional (3D) process models based on feature knowledge and geometric modeling. The 3-axis milling features are recognized as the machining objects from the boundary representation (B-Rep) part model. Geometric and parameter knowledge for modeling is derived from the machining step. In the proposed approach, two geometric modeling methods are investigated to construct feature state volumes (FSVs). The first method utilizes FSV modeling to simulate the shape of unmachined features before rough machining, while the second method utilizes FSV modeling to construct the shape of machined features before finishing machining. The case studies illustrate that the proposed approach can construct FSVs for various machining states and autonomously generate process models. In digital manufacturing, this approach assists process planners in intuitively evaluating the reasonableness of process routes. Additionally, it provides the essential driving geometry required for the autonomous generation of tool paths.

CAPP-GPT: A computer-aided process planning-generative pretrained transformer framework for smart manufacturing

Smart manufacturing (SM) constitutes the backbone of Industry 4.0 (I4.0), allowing for heightened autonomy of the various interacting cyber-physical systems, making the various entities on the production floor. Connectivity, a vital enabler, plays a crucial role through state-of-the-art Digital Twinning (DT) technologies driven by underlying innovations like the industrial Internet of Things, Cloud Computing, and advancements in sensory devices. DT, which plays a vital role in the various planning functions under the production and operations management umbrella, is being used in the developed combined CAPP-GPT (Computer-Aided Process Planning-Generative Pretrained Transformer) and production scheduling approach to address disruptions on the shopfloor and in self-healing of the manufacturing processes at a micro-CAPP level by optimally adapting the process parameters and the developed toolpath on the fly based on online process signature measurements. In a leap commensurate with that which has taken place in Natural Language Processing-Large Language Models (Chat-GPT), similar efforts are currently being undertaken to parse CAD data structures and blueprints, fusing operations research and predictive analytics algorithms to carry out setup planning as well as sequencing and grouping manufacturing sub-operations. A hybridized Optimization and Machine Learning (ML) approach is employed where Logical Analysis of Data is used to solve the problem heuristically, exploiting various generative and variant methods at heart. Another extension of this macro-CAPP problem is being tackled by integrating the problem with delayed product differentiation, lot-sizing, and transfer line balance for futuristic batch-production shops employing Hybrid Manufacturing (HM) and Smart Assembly. At the micro-CAPP level, HM process parameters are optimized using a comprehensive approach employing the Taguchi loss function to assess surface roughness, internal failure costs, and other criteria, including greenhouse gas emissions and expended energy. Online measurements of the process signatures are also employed to adapt the initial set of process parameters using different automatic control schemes. ML is used to identify the process parameters carrying simulations on Simulink before the system is deployed.

Batch-sizing and machinability data systems for milling operations: An optimal sustainable cost of quality approach

Nowadays, manufacturers make every effort to achieve a higher quality of their products at an attractive cost. With all the introduced legislation and incentives in the developed world to address global warming, machining shops in the West also strive to cut greenhouse emissions. This article offers an optimal approach to the micro Computer-Aided Process Planning (CAPP) problem to optimize the internal quality cost and buffering effect while keeping the environmental impact low. To optimize the machining parameters, the mathematical model is developed for different milling operations, face, side, and peripheral. cutting speed, feed rate, axial depth of cut, radial depth of cut, nose radius, and batch sizing while maximizing profit and meeting customer demand. A Mixed-Integer Nonlinear Programming (MINLP) model is formulated and solved using Classical Constrained Nonlinear Optimization (CCNO) and Genetic Algorithms (GAs). Surface roughness, used as a metric to evaluate the desired quality level of a finished machined part type, is modeled as a Gaussian random variable to model the surface roughness of the machined part utilizing a cumulative normal distribution. The ratio of rework and scrap is calculated in terms of the surface roughness of the machined part shifting away from the target and exceeding upper and lower specification limits. The internal failure cost model, addressing both scrap and rework, is developed based on Taguchi’s quadratic loss function. CCNO is employed to validate the results obtained by GAs, relaxing the lot-sizing integrality constraint and, thus, the convexity of the produced relaxed model. An iterative method employing a developed multi-regression model is used to solve for the expended power consumption (an inherent highly nonlinear environmental criterion of the developed model) within both GAs and CCNO. This study reveals that the machining parameters substantially impact the cost components of the objective function as well as the scrap and rework quantities. A stringent quality cost target can force the model to optimize the feed rate and nose radius to minimize the internal failure quality cost while improving the environmental impact, including direct and indirect power consumption and CO2 emissions considerations.

High-temperature heat pump working fluid development based on molecular design and the group contribution method

High-temperature heat pumps are widely recognized as a critical approach for decarbonizing energy-intensive industries, and their performance is significantly influenced by the working fluids. However, conventional working fluid selection methods primarily focus on existing fluids, limiting the selection scale. Therefore, a group-contribution-based computer-aided molecular design method has been proposed to design and select working fluids for high-temperature heat pumps on a large scale, comprehensively considering environmental impact, safety, operating reliability and performance. A total of 9771 working fluids were generated by the computer-aided process based on the backtracking searching algorithm. Under different working conditions, 208, 232, 243, 228, and 208 working fluids have passed the multi-level screening process based on the group contribution method, respectively, with an overall passing rate below 2.49 %. The H(C)FOs among the top 50 working fluids, ranked by the coefficient of performance, were further analyzed. Statistically, 92.4 % and 98.73 % of the total top 50 working fluids had their Tbr and Tc values within the ranges of 0.640–0.675 and 432–480 K, respectively. Among the 18 HCFOs within the top 50 list, 83.3 % were non-existent. R1251zd and R1344zf demonstrated the best ranking results, while R1242zf, R1233yf, and R1233xf exhibited superior working condition flexibility. As an existing HCFO, R1251yd outperformed other working fluids. Of the 35 HFOs in the top 50 list, 88.57 % were non-existent, and R1381myz, R1371my and R1381mzz showed both exceptional performance and superior working condition adaptability. In conclusion, there remain promising prospects and a notable necessity for the new H(C)FO development, and guidance can be expected from this study in this regard.

ФОРМАЛИЗАЦИЯ ПРОЦЕДУРЫ КАЛИБРОВКИ ИНСТРУМЕНТА ДЛЯ ТЕХНОЛОГИЧЕСКОЙ ПОДГОТОВКИ СБОРОЧНОГО ПРОИЗВОДСТВА В АВТОМОБИЛЕСТРОЕНИИ

The statistical parameters of failing assembly equipment of various automotive industry enterprises operating under similar conditions do not differ from each other, this fact indicates the need to analyze and find common solutions to the problems posed. When planning the calibration frequency of new assembly equipment, failure statistics from existing production facilities can be used, in particular for calibrating the tightening torque in periodic inspection. The materials presented in the article assume formalizing technological preparation of production in terms of ensuring continuous assembly. The paper provides a formal description for calculating the calibration schedule of an assembly tool, the estimated time for the tightening torque to exceed the tolerance range, the time for periodic calibration of the tool and the dependencies for evaluating the frequency of checks. The presented results can be used to build computer-aided process planning for assembly plants in the automotive industry; software and hardware solutions are developed on their basis.

Power-to-ammonia synthesis process with membrane reactors: Techno- economic study

This work aims at investigating the potential of catalytic membrane reactors (CMRs) for ammonia production from renewable hydrogen. To achieve this objective, computer-aided process simulation is deployed using Aspen plus v14 simulation tool. A 1D model is developed to describe the integration of a ruthenium catalyst into a CMR equipped with inorganic membranes that are selective to NH3 over N2 and H2. First, the CMR performance is studied across a broad spectrum of membrane properties and operating conditions. Subsequently, the CMR model is integrated into several Power-to-Ammonia (PtA) process layouts, which are optimized and techno-economically compared against a traditional PEM-based PtA production process, employing condensation for the purification stage. Key findings at the reactor level confirmed that beyond a selectivity threshold towards hydrogen of 10–20, both the degree of conversion and recovery tend to be generally independent of the membrane's selectivity. However, for producing pure ammonia permeate, highly selective membranes (>1000 towards H2) depending on the pressure drop are found essential making this alternative elusive based on existing membranes. At the PtA level, results in terms of efficiency indicates that the use of membrane reactor increases the system efficiency by around ∼8% with respect to reference case while ∼15% enhancement is achieved with this process when using SOEC technology. When examining the final Levelized Cost of Ammonia (LCOA), incorporating a membrane reactor in conjunction with condensation separation results in a cost that nearly matches the reference case. Future research should focus on replacing the condensation process with a method of purification at lower pressures. This could potentially further improve efficiency and reduce the cost of the membrane reactor compared to the traditional one.

Adaptive recognition of machining features in sheet metal parts based on a graph class-incremental learning strategy

The integration of computer-aided design (CAD), computer-aided process planning (CAPP), and computer-aided manufacturing (CAM) systems is significantly enhanced by employing deep learning-based automatic feature recognition (AFR) methods. These methods outperform traditional, rule-based approaches, particularly in handling the complexities of intersecting features. However, existing deep learning-based AFR methods face two major challenges. The initial challenge stems from the frequent utilization of voxelized or point-cloud representations of CAD models, resulting in the unfortunate loss of valuable geometric and topological information inherent in original Boundary representation (B-Rep) models. The second challenge involves the limitation of supervised deep learning methods in identifying machining features that are not present in the predefined dataset. This constraint renders them suboptimal for the continually evolving datasets of real industrial scenarios. To address the first challenge, this study introduces a graph-structured language, Multidimensional Attributed Face-Edge Graph (maFEG), crafted to encapsulate the intricate geometric and topological details of CAD models. Furthermore, a graph neural network, Sheet-metalNet, is proposed for the efficient learning and interpretation of maFEGs. To tackle the second challenge, a three-component incremental learning strategy is proposed: an initial phase of pre-training and fine-tuning, a prototype sampling-based replay, and a stage employing knowledge distillation for parameter regularization. The effectiveness of Sheet-metalNet and its complementary incremental learning strategy is evaluated using the open-source MFCAD++ dataset and the newly created SMCAD dataset. Experimental results show that Sheet-metalNet surpasses state-of-the-art AFR methods in machining feature recognition accuracy. Moreover, Sheet-metalNet demonstrates adaptability to dynamic dataset changes, maintaining high performance when encountering newly introduced features, thanks to its innovative incremental learning strategy.

Practical exercises of computer-aided process synthesis for chemical engineering undergraduates

The study presents ten different exercises covering various computational tools. These exercises are practical applications presented to improve the understanding and skills of students in important concepts of chemical-aided process synthesis. A few exercises aim to build a foundation in computational techniques for chemical engineering undergraduates. The exercises are based on a spreadsheet that covers the design of regression analysis to find the optimum Antoine constants, array calculation for multicomponent distillation material balance, and the generation of a Gantt chart to plan and study the activities of batch processes. The other exercises included an introduction to process simulation, simulation, and reactor rating, and a simulation of multicomponent shortcut distillation. These exercises provide students with hands-on experience in utilizing process simulation software essential for analysing and optimizing chemical processes in real-world scenarios. The exercises also included the design of a heat exchanger network and solving a linear programming problem. An anonymous survey was collected from the cohort that had undergone the exercises, and the practical grades were compared with the batch that did not study the proposed exercises. Additionally, student feedback on practical exercises was collected. Based on the experience of the course coordinator and the collected feedback from participants, it was clear that the exercises helped students to inculcate critical thinking and self-learning abilities. An article essentially sheds light on the computer-aided practical exercises that enable chemical engineering graduates to engage in lifelong learning.

Molecular property prediction based on graph structure learning.

Molecular property prediction (MPP) is a fundamental but challenging task in the computer-aided drug discovery process. More and more recent works employ different graph-based models for MPP, which have achieved considerable progress in improving prediction performance. However, current models often ignore relationships between molecules, which could be also helpful for MPP. For this sake, in this article we propose a graph structure learning (GSL) based MPP approach, called GSL-MPP. Specifically, we first apply graph neural network (GNN) over molecular graphs to extract molecular representations. Then, with molecular fingerprints, we construct a molecule similarity graph (MSG). Following that, we conduct GSL on the MSG, i.e. molecule-level GSL, to get the final molecular embeddings, which are the results of fuzing both GNN encoded molecular representations and the relationships among molecules. That is, combining both intra-molecule and inter-molecule information. Finally, we use these molecular embeddings to perform MPP. Extensive experiments on 10 various benchmark datasets show that our method could achieve state-of-the-art performance in most cases, especially on classification tasks. Further visualization studies also demonstrate the good molecular representations of our method. Source code is available at https://github.com/zby961104/GSL-MPP.

BrepMFR: Enhancing machining feature recognition in B-rep models through deep learning and domain adaptation

Feature Recognition (FR) plays a crucial role in modern digital manufacturing, serving as a key technology for integrating Computer-Aided Design (CAD), Computer-Aided Process Planning (CAPP) and Computer-Aided Manufacturing (CAM) systems. The emergence of deep learning methods in recent years offers a new approach to address challenges in recognizing highly intersecting features with complex geometric shapes. However, due to the high cost of labeling real CAD models, neural networks are usually trained on computer-synthesized datasets, resulting in noticeable performance degradation when applied to real-world CAD models. Therefore, we propose a novel deep learning network, BrepMFR, designed for Machining Feature Recognition (MFR) from Boundary Representation (B-rep) models. We transform the original B-rep model into a graph representation as network-friendly input, incorporating local geometric shape and global topological relationships. Leveraging a graph neural network based on Transformer architecture and graph attention mechanism, we extract the feature representation of high-level semantic information to achieve machining feature recognition. Additionally, employing a two-step training strategy under a transfer learning framework, we enhance BrepMFR's generalization ability by adapting synthetic training data to real CAD data. Furthermore, we establish a large-scale synthetic CAD model dataset inclusive of 24 typical machining features, showcasing diversity in geometry that closely mirrors real-world mechanical engineering scenarios. Extensive experiments across various datasets demonstrate that BrepMFR achieves state-of-the-art machining feature recognition accuracy and performs effectively on CAD models of real-world mechanical parts.

Computer Aided Process Planning for Sheet Metal Cutting Operations in the Manufacturing Industry

This paper describes computer-aided process planning for sheet metal cutting operations. The two designs to production stages that is highlighted are sheet metal processing and machining. There are four modules in this object's system. Virtual factory environments, feature-based designs, process planning, and process-based feature mapping. Feature-based design is utilized for the conception, modeling, and representation of the components for manufacturing applications. Whenever it involves sheet metal cutting operations in the manufacturing sector, computer-aided process planning, is extremely important for streamlining manufacturing procedures. The provides a general introduction to computer-aided process planning as it relates to sheet metal cutting, emphasizing its importance in boosting productivity, saving costs, and raising product quality. The main goal of computer-aided process planning for sheet metal cutting is to integrate computer technology, CAD/CAM systems, and sophisticated algorithms to automate and streamline the planning process. Based on design requirements, material characteristics, and production limitations, this method enables manufacturers to produce exact and ideal cutting plans. To create blanking and piercing holes, stamped or punched die are utilized in generative shape design; the generative computer-aided process planning system is created in C++ and used in various case studies presented in the present work. The application of computer-aided process planning for sheet metal cutting has several advantages, including greater output, less material waste, shortened lead times, and improved competitiveness in the industrial sector. The potential of computer-aided process planning to revolutionize sheet metal cutting operations, making them more efficient and cost-effective while ensuring high-quality final products is highlighted in the present research.

Knowledge graph with deep reinforcement learning for intelligent generation of machining process design

In the manufacturing industry, the design of machining processes plays a pivotal role in determining the quality, efficiency, and cost of product manufacturing. Machining process design is progressing towards intelligence to meet the high demands for efficiency and effectiveness in smart manufacturing. Building upon traditional computer-aided process planning, intelligent technologies, such as machine learning and knowledge graphs, have emerged as key drivers in advancing intelligent process design. To address knowledge accumulation, inflexible reuse, and fragmented reasoning in machining process design, this paper organically integrates the structured representation characteristics of the knowledge graph and the perceptual reasoning capabilities of deep reinforcement learning. It introduces an intelligent generation method for machining process design based on knowledge graph and deep reinforcement learning, aiming to achieve unified representation and reasoning reuse for historical process cases and general process rules. The effectiveness of the proposed method is validated through a case study involving the generation of machining process solutions for a specific model of diesel engine components. This research contributes valuable insights to overcoming the limitations of traditional methods and enhancing the efficiency of the machining process design.

Biogas upgrading—Computer-aided ionic liquid absorbent design and process evaluation

Biogas is a renewable energy source and needs to be upgraded to biomethane for injection into the natural gas grid or use as fuel. To design ionic liquid (IL) solvents for biogas upgrading, a computer-aided ionic liquid design (CAILD) method and the corresponding process simulation are presented. The UNIFAC-IL model is employed to calculate the solubility of gases in ILs, while group contribution (GC) based models are used to predict the physicochemical properties of ILs. By using the performance index (PI) as the objective function and the structural feasibility and physicochemical properties as constraints, a mixed-integer nonlinear programming (MINLP) problem is formulated and solved by the generate-and-test method. Two IL solvents, [MMPY][Tf2N] (1,3-dimethylpyridinium bis(trifluoromethylsulfonyl)imide) and [MMPY][eFAP] (1,3-dimethylpyridinium tris(pentafluoroethyl) trifluorophosphate), are found to be the optimal IL solvents from 880 IL candidates. To perform the process simulation by using the designed ILs, the parameters for the equations of calculation of required properties are regressed. A sensitivity analysis is performed to find the optimal conditions for the process. Finally, the developed process is compared with the water-scrubbing process for biogas upgrading.

A Mathematica-Based Interface for the Exploration of Inter- and Intra-Regional Financial Flows

This work surveys the use of directed weighted graphs in conducting comparative static analyses. The paper discusses the implementation of a computer-aided process for building spreadsheet-based graph models for inter- and intra-regional financial flows. The graph-theoretic techniques are programmed to enable the interactive visualization and analysis of financial data using Wolfram technologies (i.e., Mathematica software v. 11.3 or later, Wolfram player v. 12 or later). The paper describes the workflow for several interactive visualizations applicable to financial networks. The author provides four programs, written in the Wolfram language, that customize input–output financial models by combining the Manipulate command with built-in Mathematica functions and functions of the IGraph package (IGraph/M). The study is a tutorial article for the generation of a suite of visual schemes that provide patterns, practices, and roadmaps of the financial markets across the globe.

Brain Tumor Detection and Categorization with Segmentation of Improved Unsupervised Clustering Approach and Machine Learning Classifier.

There is no doubt that brain tumors are one of the leading causes of death in the world. A biopsy is considered the most important procedure in cancer diagnosis, but it comes with drawbacks, including low sensitivity, risks during biopsy treatment, and a lengthy wait for results. Early identification provides patients with a better prognosis and reduces treatment costs. The conventional methods of identifying brain tumors are based on medical professional skills, so there is a possibility of human error. The labor-intensive nature of traditional approaches makes healthcare resources expensive. A variety of imaging methods are available to detect brain tumors, including magnetic resonance imaging (MRI) and computed tomography (CT). Medical imaging research is being advanced by computer-aided diagnostic processes that enable visualization. Using clustering, automatic tumor segmentation leads to accurate tumor detection that reduces risk and helps with effective treatment. This study proposed a better Fuzzy C-Means segmentation algorithm for MRI images. To reduce complexity, the most relevant shape, texture, and color features are selected. The improved Extreme Learning machine classifies the tumors with 98.56% accuracy, 99.14% precision, and 99.25% recall. The proposed classifier consistently demonstrates higher accuracy across all tumor classes compared to existing models. Specifically, the proposed model exhibits accuracy improvements ranging from 1.21% to 6.23% when compared to other models. This consistent enhancement in accuracy emphasizes the robust performance of the proposed classifier, suggesting its potential for more accurate and reliable brain tumor classification. The improved algorithm achieved accuracy, precision, and recall rates of 98.47%, 98.59%, and 98.74% on the Fig share dataset and 99.42%, 99.75%, and 99.28% on the Kaggle dataset, respectively, which surpasses competing algorithms, particularly in detecting glioma grades. The proposed algorithm shows an improvement in accuracy, of approximately 5.39%, in the Fig share dataset and of 6.22% in the Kaggle dataset when compared to existing models. Despite challenges, including artifacts and computational complexity, the study's commitment to refining the technique and addressing limitations positions the improved FCM model as a noteworthy advancement in the realm of precise and efficient brain tumor identification.

Zeolite-Y-catalyst production from locally sourced Meta-kaolin: Computer-Aided scale-up process design and economic analysis with Monte-Carlo-Simulation

Production of zeolite-Y catalyst from natural substrate has been a research trend in the scientific community. Published articles revealed that zeolite-Y recovery from locally sourced metakaolin is confined to laboratory practice. Scale-up process design and its economic feasibility for zeolite-Y catalyst recovery are rarely found in the scientific bibliography. Therefore, this study presented conceptual scale-up process design, base-case techno-economics and Monte-Carlo simulation of zeolite Y recovery from Nigerian metakaoline. ASPEN Base Case Simulation (ABCS), scale-up design and economics were accomplished using inherent design and costing algorithms in ASPEN Batch Process Developer (ABPD) V10. Process economic parameters: Net Present Value (NPV), Internal Rate of Return (IRR), Return on Investment (ROI) and Payback Time (PBT), were modelled and optimized using Design Expert V13 software; while zeolite Unit Production Cost (UPC), Annual Production Cost (APC), Total Capital Investment (TCI) and interest/discount rate were considered as model inputs. Monte-Carlo Simulation (MCS) in Crystal Ball Oracle software was used to perform the sensitivity and uncertainty analyses. The base-case techno-economic results of process design of 600,000 kg/year zeolite production gave batch size 5000 kg/batch with 104 batches/year, batch time (4149 min), TCI ($15,930,306), APC ($147,145), NPV ($41,983,375), ROI (38.13 %) and PBT (2.14 years). The coefficient of determination (R2) of the economic models were 0.9978, 0.9989 and 0.9986 for NPV, ROI and IRR respectively. The optimum economic variables that maximized synthesis of 5000 kg/batch zeolite Y are UPC ($11.68), APC ($100,033) and TPC ($15,930,200). MCS uncertainty for NPV, IRR and ROI are negligible. Therefore, this study demonstrated that scale-up zeolite-Y production from the local substrate is economically feasible.

Prediction of stability constants of metal-ligand complexes by machine learning for the design of ligands with optimal metal ion selectivity.

The new LOGKPREDICT program integrates HostDesigner molecular design software with the machine learning (ML) program Chemprop. By supplying HostDesigner with predicted logK values, LOGKPREDICT enhances the computer-aided molecular design process by ranking ligands directly by metal-ligand binding strength. Harnessing reliable experimental data from a historic National Institute of Standards and Technology (NIST) database and data from the International Union of Pure and Applied Chemistry (IUPAC), we train message passing neural net algorithms. The multi-metal NIST-based ML model has a root mean square error (RMSE) of 0.629 ± 0.044 (R2 of 0.960 ± 0.006), while two versions of lanthanide-only IUPAC-based ML models have, respectively, RMSE of 0.764 ± 0.073 (R2 of 0.976 ± 0.005) and 0.757 ± 0.071 (R2 of 0.959 ± 0.007). For relative logK predictions on an out-of-sample set of six ligands, demonstrating metal ion selectivity, the RMSE value reaches a commendably low 0.25. We showcase the use of LOGKPREDICT in identifying ligands with high selectivity for lanthanides in aqueous solutions, a finding supported by recent experimental evidence. We also predict new ligands yet to be verified experimentally. Therefore, our ML models implemented through LOGKPREDICT and interfaced with the ligand design software HostDesigner pave the way for designing new ligands with predetermined selectivity for competing metal ions in an aqueous solution.