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Applied Machine Learning in Chemical
Process Engineering

Applied Machine Learning in Chemical Process Engineering

A Practical Approach

This book is a comprehensive guide that immerses you into the world of building and deploying AI-powered solutions. You are introduced to the core architecture of LLMs and equipped with essential Best Data Practices (BDPs) for utilizing Generative AI responsibly, ensuring ethical and efficient AI deployment.The book starts with the foundational aspects of Generative AI application development. You will learn the nuances of data handling in Generative AI apps, from working with embeddings to managing unstructured and structured data, and handling Personally Identifiable Information (PII) data. The exploration extends to understanding the differences between deterministic and LLM-based data synthesis and integrating Generative AI apps with enterprise data, providing you with practical insights into leveraging data effectively for intelligent applications. A chapter on prompt engineering explains the importance of prompts in AI interactions, covering a spectrum of techniques and pitfalls while offering exercises to enhance prompt engineering skills. As you progress through the book, you take a journey from conceptualization to production and deployment of Generative AI applications. You discover the essentials of Generative AI application development, gain insights into the pathway from ideation to production, and explore the intricacies of LLM selection and fine-tuning.The book equips you with the knowledge and tools necessary to navigate the complex terrain of AI development and deployment, making it an indispensable resource for AI enthusiasts, developers, and business leaders alike.What You Will Learn<ul><li>Know the core architecture of LLMs and how these models have revolutionized AI applications</li><li>Handle various data types, including unstructured, structured, and personally identifiable information (PII) data in Generative AI applications</li><li>Understand prompt engineering and its importance in AI interactions and applications</li><li>Understand modular design of Generative AI apps, essential backend and frontend components, and the unique principles guiding Generative AI app design</li></ul>&nbsp;&nbsp;

This book is a comprehensive guide that immerses you into the world of building and deploying AI-powered solutions. You are introduced to the core architecture of LLMs and equipped with essential Best Data Practices (BDPs) for utilizing Generative AI responsibly, ensuring ethical and efficient AI deployment.The book starts with the foundational aspects of Gehatnerative AI application development. You will learn the nuances of data handling in Generative AI apps, from working with embeddings to managing unstructured and structured data, and handling Personally Identifiable Information (PII) data. The exploration extends to understanding the differences between deterministic and LLM-based data synthesis and integrating Generative AI apps with enterprise data, providing you with practical insights into leveraging data effectively for intelligent applications. A chapter on prompt engineering explains the importance of prompts in AI interactions, covering a spectrum of techniques and pitfalls while offering exercises to enhance prompt engineering skills. As you progress through the book, you take a journey from conceptualization to production and deployment of Generative AI applications. You discover the essentials of Generative AI application development, gain insights into the pathway from ideation to production, and explore the intricacies of LLM selection and fine-tuning.The book equips you with the knowledge and tools necessary to navigate the complex terrain of AI development and deployment, making it an indispensable resource for AI enthusiasts, developers, and business leaders alike.&nbsp;What You Will Learn<ul><li>Know the core architecture of LLMs and how these models have revolutionized AI applications</li><li>Handle various data types, including unstructured, structured, and personally identifiable information (PII) data in Generative AI applications</li><li>Understand prompt engineering and its importance in AI interactions and applications</li><li>Understand modular design of Generative AI apps, essential backend and frontend components, and the unique principles guiding Generative AI app design</li></ul>&nbsp;Who This Book Is ForFrom AI enthusiasts exploring the field to software developers seeking practical insights, and business leaders looking to harness the power of AI for organizational growth and innovation&nbsp;&nbsp;

Building and Deploying Ethical AI-Powered Solutions

Practical Generative AI: From Concept to Deployment

FinTech and the Remaking of Financial Institutions, Second Edition explores the transformative potential of new entrants and innovations on business models. In its survey and analysis of FinTech, the book addresses current and future states of money and banking. It provides broad contexts for understanding financial services, products, technology, regulations, and social considerations. The Second Edition expands and updates topics covered in the first edition, with particular emphasis on current and expected impact of AI; maturation of crypto and consequent regulatory issues; international developments; and the continued integration of technology advances in large financial institutions. The book shows how FinTech has evolved and will drive the future of financial services. It sheds new light on disruption, innovation and opportunity by placing the financial technology revolution in larger contexts. After an introductory chapter, chapters 2-12 present a necessary background of economics and finance as well as selected Fintech companies that are active in specific markets. Chapters 13-17 then broaden the scope to discuss the financing of startups; FinTech activity in hubs outside of the United States; financial regulation; negative impacts of FinTech on some social issues; and finally, responses of the Big Financial Institutions to the FinTech disruption. FinTech and the Remaking of Financial Institutions, Second Edition is a valuable resource for students, researchers, and professionals alike who need to understand the latest emerging FinTech technologies, how they’re used in finance, how they are regulated, and the societal implications.

Fintech and the Remaking of Financial Institutions

Up-to-date reference enabling readers to address the full spectrum of AI security challenges while maintaining model utility Generative AI Security: Defense, Threats, and Vulnerabilities delivers a technical framework for securing generative AI systems, building on established standards while focusing specifically on emerging threats to large language models and other generative AI systems. Moving beyond treating AI security as a dual-use technology, this book provides detailed technical analysis of three critical dimensions: implementing AI-powered security tools, defending against AI-enhanced attacks, and protecting AI systems from compromise through attacks like prompt injection, model poisoning, and data extraction. The book provides concrete technical implementations supported by real-world case studies of actual AI system compromises, examining documented cases like the DeepSeek breaches, Llama vulnerabilities, and Google's CaMeL security defenses to demonstrate attack methodologies and defense strategies while emphasizing foundational security principles that remain relevant despite technological shifts. Each chapter progresses from theoreticalfoundations to practical applications. The book also includes an implementation guide and hands-on exercises focusing on specific vulnerabilities in generative AI architectures, security control implementation, and compliance frameworks. Generative AI Security: Defense, Threats, and Vulnerabilities discusses topics including: <ul> <li>Machine learning fundamentals, including supervised, unsupervised, and reinforcement learning and feature engineering and selection</li> <li>Intelligent Security Information and Event Management (SIEM), covering AI-enhanced log analysis, predictive vulnerability assessment, and automated patch generation</li> <li>Deepfakes and synthetic media, covering image and video manipulation, voice cloning, audio deepfakes, and AI's greater impact on information integrity</li> <li>Security attacks on generative AI, including jailbreaking, adversarial, backdoor, and data poisoning attacks</li> <li>Privacy-preserving AI techniques including federated learning and homomorphic encryption</li> </ul> Generative AI Security: Defense, Threats, and Vulnerabilities is an essential resource for cybersecurity professionals and architects, engineers, IT professionals, and organization leaders seeking integrated strategies that address the full spectrum of Generative AI security challenges while maintaining model utility.

Triangulating AI Security

Previous research on finite/fixed/predefined-time control and its relevant sliding mode structures focus on forcing a system state (vector) to converge within a certain time moment, regardless of how each state element converges. Time-Synchronized Control and Applications for Autonomous Systems introduces a control problem with unique finite/fixed/predefined-time stability considerations, namely time-synchronized stability, where at the same time, all the system state elements converge to the origin. This book also delves into the wide-ranging applications of time-synchronized control in various practical control systems, including but not limited to spacecraft attitude control/tracking, satellite orbit control, dynamic positioning of vessels, cooperative control of autonomous vehicles, etc. In addition, a diverse array of control system models are considered, encompassing disturbed systems, chaotic systems, multi-input multi-output (MIMO) systems, nonlinear systems, and Euler-Lagrange systems. Through an in-depth exploration of these specific cases, this book showcases the versatility and effectiveness of time-synchronized control methods across different domains, providing readers with a comprehensive perspective on their practical utility.

Time-Synchronized Control and Applications for Autonomous Systems

Advances in Industrial and Systems Engineering

Advances in Human-AI Collaboration

Six Dimensions of Business Dark Data

Agentic Hyper-Personalized Dimensions

Medical Robots and Devices: New Developments and Advances

AI and Data Science in Medical Research focuses on the integration of AI and data science into medical research, highlighting their impact on drug discovery, medical imaging, diagnostics, and genomic medicine. The book addresses the acceleration of therapeutic compound discovery and optimization of drug development pipelines through AI. The volume also discusses advancements in medical imaging, including early disease detection and neuroimaging. Additionally, it covers the application of AI in genomic medicine, offering insights into personalized treatment strategies. The volume concludes with an examination of AI's role in public health surveillance, particularly in disease detection and epidemiological research.

AI and Data Science in Medical Research

Advances in Nonlinear Dynamical Systems and Robotics (ANDC)

Swarm Robotics Solutions for National Security and Marine Forensics explores the cutting-edge applications of swarm robotics in safeguarding national security and advancing marine forensics. This book delves into the innovative use of collaborative robotic systems inspired by nature, where multiple autonomous agents work together seamlessly to address complex challenges. From enhancing surveillance and reconnaissance capabilities for national defence to revolutionizing marine forensic investigations, this comprehensive guide highlights the transformative impact of swarm robotics on critical domains. A must-read for researchers, practitioners, and enthusiasts in robotics, artificial intelligence, and security, this book illuminates the future possibilities of swarm robotics in shaping a safer and more secure world.

Swarm Robotics Solutions for National Security and Marine Forensics

Enhancing Reflective Teaching in Higher Education

Redefining Teaching and Learning

GenAI in Higher Education

AI and Data Science in Precision Medicine, Predictive Analytics, and Medical Practice explores the transformative role of AI and data science in enhancing precision medicine, predictive analytics, and medical practice. The book covers diverse topics such as AI-driven personalized medicine, seizure prediction through EEG analysis, and the application of chaos theory in AI-driven healthcare. The volume also delves into medical practice and education, including ethical considerations, AI-driven supply chain management, and clinical documentation using natural language processing. Furthermore, it examines AI's role in telemedicine, patient engagement, and adherence, offering innovative solutions to improve healthcare delivery and outcomes.

AI and Data Science in Precision Medicine, Predictive Analytics, and Medical Practice

Waste Biorefinery Technologies for Sustainable Development: Volume 1: Thermochemical Processes deals with the intersection of circular economy principles and biorefinery innovation, exploring how biorefineries can play a central role in closing the loop of resource use and promoting sustainable development. Through a comprehensive examination of circular economy concepts and biorefinery technologies, it illustrates how waste streams can be valorized and transformed into valuable products through efficient and sustainable processes. From organic wastes to agricultural residues and industrial by-products, it covers how diverse biomass feedstocks can be utilised within circular biorefinery systems to produce bioenergy, biochemicals, biomaterials, and bioplastics. The volumes cover the basic components of biorefineries and various approaches required for the valorization of versatile feedstocks which make an indispensable resource for (under)graduate students. The discussion of role of biorefineries in addressing global challenges such as climate change and resource depletion, current research trends, and future directions offers valuable guidance for researchers seeking to deepen their understanding and identify opportunities for further investigation. Volume 1 of this two-volume set deals with thermochemical processes, and includes an introduction to circular biorefinery, biomass preparation &amp; pretreatment, and thermochemical routes for converting biomass into bioproducts and biofuels.

Volume 1: Thermochemical Processes

Waste Biorefinery Technologies for Sustainable Development

Introduction to the surface renewal model of mass transfer in the analysis/design of gas-liquid contacting equipment and membrane filters Applications of the Surface Renewal Model of Mass Transfer provides a rigorous application of the surface renewal theory of mass transfer to describe physical and chemical gas absorption and membrane filtration. This book demonstrates that the surface renewal model can predict the experimentally measured liquid-side physical mass-transfer coefficient in gas absorption with a fair degree of accuracy, shows that the surface renewal model can correlate permeate flux and transmembrane pressure drop data in constant pressure and constant flux microfiltration, and contains numerous examples of the application of the model to real-world situations. This book includes information on: <ul> <li>Applications of the surface renewal model in fields like chemical engineering, and oceanography.</li> <li>The complex surface renewal model as a better description of the turbulent hydrodynamics that prevail at the gas-liquid interface interface compared to the film model.</li> <li>Measurements of the liquid-side physical mass-transfer coefficient in gas absorption studies and surface-age distributions in wind-wave tanks.</li> <li>Flow instabilities induced by wall roughness or spacers or by their deliberate introduction into the main flow in membrane filtration</li> <li>Analysis and design of gas-liquid contactors (stirred tanks and packed towers) and membrane filters using a mass-transfer approach</li> </ul> Applications of the Surface Renewal Model of Mass Transfer is an excellent, first-of-its-kind reference for researchers in academia and industry, along with advanced students in chemical engineering, environmental engineering, bioprocess/biological engineering, paper engineering, and related programs of study.

Applications of the Surface Renewal Model of Mass Transfer

Applied Machine Learning in Chemical Process Engineering

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