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The molecular and technological advances in the bioprocess industry have witnessed a major change over the past few decades and the bioprocess industry is not an exception. While considering the same the current book brings together a spectrum of technologies that have contributed significantly to the various industrial production processes, allowing sustainable development and supporting economic growth. Further, strain engineering has been an indispensable part of the bioprocess sector. With the potential to contribute to the global economy, engineering high-performing microbial strains efficiently and economically is critical. Thus, the design–Build–Test– Learn (DBTL) framework in integration with Artificial Intelligence (AI) and genome engineering breakthroughs has been discussed enabling to gain an insight into the integrated strategy to accelerate strain development, optimize productivity, and support a burgeoning bioeconomy. In addition, "omics" technologies—genomics, transcriptomics, proteomics, and metabolomics—are thoroughly examined for their role in bioprocess optimization and have been discussed in the current boo. Their contributions to metabolic engineering and synthetic biology lay the foundation for nextgeneration bioprocess development, driving advances that once seemed unattainable. To extend the knowledge further, gene editing, particularly with revolutionary tools such as CRISPR is known for its transformative impact on microbial strain enhancement. This exploration includes the creation of genetically modified organisms (GMOs) that produce vital pharmaceuticals, biopesticides, and biofuels, directly contributing to global food security, public health, and economic resilience. However, the narrative remains grounded, addressing challenges like regulatory concerns and societal acceptance. The incorporation of AI into biorefineries is presented as a game-changing strategy. AI models such as Machine Learning and Neural Networks enhance real-time monitoring, optimize resource utilization, and refine sustainability assessments, even as integration challenges persist. This synergy of digital technology and biotechnology signals a new era of smart bioprocessing. Recognizing the complexities inherent in bioprocessing, the book also provides an insight into safety and regulatory frameworks. It advocates for Inherently Safer Design (ISD), adherence to ALCOA+ principles, and the adoption of global best practices to ensure worker safety, environmental protection, and operational integrity. Lastly, the book provides an understanding of the

Introduction  A broad definition of bioprocessing is the process of creating a valuable product from a live source. The system’s most important element is that the source organism is living and sensitive to its surroundings. Accordingly, the framework states that it will respond to relatively small changes in its physico-chemical environment by modifying its physiology to maximise efficiency. The use of entire cells and enzymes to catalyse bioprocesses in industrial settings is becoming more popular these days than conventional chemical synthetic methods. Among the many benefits of biotransformations in this context are their extensive substrate portfolio (including liquid, solid, and gas waste), one-pot reactions in mild circumstances and environmental friendliness. However, a number of drawbacks, including the unstable nature of biocatalysts and subpar performance in specific reaction conditions, the low solubility of some substrates in the reaction medium, substantial production expenses brought on by intricate downstream processing and product isolation and the lack of expertise in microbiology or bioprocess design, frequently make it difficult to scale up bioprocesses from the lab to the manufacturing facility. There is a growing need for innovative bioprocesses that can get over the aforementioned obstacle in industrial and environmental biotechnology in order to create effective, sustainable, and affordable processes. As a result, the industrial sector requires new methods that use biocatalysts that are highly active and stable under a variety of reaction conditions (typically accomplished through metabolic engineering), boost feedstock utilisation by using inexpensive waste resources, use green chemistry solutions that enable the use of fewer toxic solvents and reagents, or develop cost-effective biorefinery concepts that can transform industrial waste and byproducts into products with added value, thereby establishing new value chains. Figure 1.1 shows the three stages of a typical bioprocess: upstream processing, bioreaction and downstream processing which includes separation and purification steps. Every stage of these phases offers multiple chances to enhance a certain bioprocess.

Introduction Wide range of industrially important products such as antibiotics, vitamins, enzymes, polymers, amino acids, biofuels are produced by microbes in extremely small quantities. Using microbial strains that generate an elevated amount of the intended product is a prerequisite for effective biotechnological procedures at the industrial scale. But since this isn’t an intrinsic feature of the chosen microorganism, changes to their genetic makeup may help to navigate this. Therefore, in order to overproduce the target metabolite and increase process efficiency, industrially relevant microorganisms are exposed to a range of treatments employing physical, chemical, or genetic methods. Thus, microbial strain improvement is the process of improving the biosynthetic capacities of microorganisms to generate the desired product in greater quantities. Recycling resources and promoting sustainable development have grown in importance in recent years. Cosmetics, food, fuels, lubricants, plastics, drinks, fibres, and medications are just a few of the goods that are being biosynthesised due to the growing popularity of biological production of commercially significant metabolites (Kaur et al., 2012). The rising bioeconomy’s success hinges on our capacity to produce high-performing strains efficiently and economically. One successful strain engineering strategy is the Design–Build– Test–Learn (DBTL) framework. The strain engineering process must be viewed from the perspective of optimising the entire cycle rather than just increasing productivity at each stage, if strain development time and cost is to drastically cut down. We suggest a strategy that incorporates all four phases of the DBTL cycle and leverages the latest developments in phenotyping techniques, high-throughput genome engineering, computational design, and machine learning tools to forecast strain scale-up efficiency. By substituting sustainable alternatives made from renewable feedstock for petroleum-based products and ingredients derived from animals or wild-harvested plants, microbial biomanufacturing of chemicals, materials and biomolecules promises to lower greenhouse gas emissions, enhance land use and promote sustainability. But doing so necessitates creating reliable and effective industrial strains that can generate a variety of goods at reasonable costs. Enhancing the effectiveness of the strain engineering process is necessary to lower development cost, time in order to take advantage of market potential in all bioeconomy industries. Over the past few decades, technical advancements in genome engineering have significantly boosted our capacity to engineer biological systems. The strain engineering process has been significantly sped and enhanced by synthetic biology methods like as genome editing using CRISPR (clustered

Introduction The eco-friendly and commercially sustainable production has been a crucial aspect of modern commercial development. The ecologically sustainable microbial production of value-added products has seen rapid growth across multiple sectors, such as food, pharmaceuticals, and energy, resulting in notable economic benefits and social advancements on a global scale (Shi et al., 2022). The development of “omics” technologies has aided the improvement of microbial production processes through diverse omics procedures, such as genomics, transcriptomics, metabolomics, and systems biology (Amer and Baidoo, 2021). These omics approaches give important understandings into the widespread metabolic variations, thereby identifying the factors that affect microbial production efficiency and improving the understanding of targeted optimization strategies. The integration of omics approaches is considered essential for any strategies that aim to be industrially sustainable. Industrial activities depend on the cultivation and accessibility of significantly competent microbial species to optimize bioproduct yields (Keasling, 2012). Recent progress in the industrial generation of value-added products heavily depends on strategies rooted in omics. Typically, the industry can achieve cost-effective production of limited quantities of biopharmaceuticals (Harris et al., 2015). Nevertheless, to sustainably scale up production capacities, economically feasible alternatives must be planned, standardised, and developed. Over the past few decades, microbes with enhanced abilities for superior product generation have been identified (Babar et al., 2018; Kim et al., 2015). Latest improvements in metabolic engineering have aided the manipulation of various genetic components, including coding and regulatory regions, to maximize commercial benefits (Babar et al., 2018). Genomic methodologies aim to investigate the variations among individuals at both the germline and somatic levels by sequencing specific genomes. The transition from DNA microarray technology to DNA sequencing has enabled the thorough sequencing of entire genomes, providing a detailed characterization of an organism’s genomic landscape (Dai and Shen, 2022) whereas, transcriptomics involves the analysis of mRNA, microRNAs, long non-coding RNAs, and circular RNAs. The methods used in transcriptomics are designed to identify and enumerate RNA molecules transcribed from a specific genome at a particular time. Proteomics on the other hand focuses on understanding the functional significance of all proteins expressed within a cell, tissue, or organism by examining the flow of information via protein signalling and metabolomics contributed a vital part in the development of bioprocesses by providing a comprehensive overview of metabolic activities (Horgan and Kenny, 2011; Petricoin et al., 2002). Synthetic biology contributes a vital part in the production of high-performance strains for microbial manufacturing. A variety of microorganisms including bacteria, fungi, and microalgae can be employed in microbial production. However, synthetic biology methods primarily emphasis on the balanced blueprint of biosynthetic metabolisms for target outcome, as well as the creation and standardisation of these pathways and the heterologous production of the biosynthetic components (Wan et al., 2023). These approaches include the optimisation of fermentation process, optimisation of medium composition, scale up of fermentation, optimization of cell growth for manufacture of desired product, and strategies to decrease feedback inhibition and product toxicity (Figure 3.1) (Wan et al., 2023; Zhang et al., 2022).

4.1 Introduction 4.1.1 Overview of Genomics in Biotechnology The genome of an organism serves as the ‘blueprint of life’ (Baeza, 2024) and functions as an information repository (Goldman & Landweber, 2016), containing the necessary information for the synthesis of molecules, cells, and tissues (Abera et al., 2017). A study of the entire collection of genes within an organism’s genome is known as genomics (NIH, 2022; WHO, 2025). Genomics encompasses aspects of genetics, focusing on the comprehensive characterization of an organism’s entire set of genes rather than on individual genes (Baeza, 2024). The investigation of living organisms has undergone a transformation through the application of genomics and related fields, providing unique insights that enhance various applications aimed at improving human life, economic conditions (Kalaitzandonakes et al., 2023), and environmental sustainability (Mudaliar et al., 2023). According to a genomic analysis, 80% of the 6,000 rare disorders have genetic causes (Brittain et al., 2017). Cancer, a genetic disease, has revealed through DNA sequencing the mutational events and oncogenic drivers (D. Wang et al., 2023). Genomic tests contribute significantly to the prediction and prevention of diseases, including familial hypercholesterolaemia (FH) (Brittain et al., 2017). Agricultural industries are currently confronting multiple challenges, including global changes in climate (Bai et al., 2024), growing populations (Putri et al., 2019), resource depletion (Feng et al., 2023), reduction of cultivable land (Mirzabaev et al., 2023), and the prevalence of pathogens (Hossain & Roslan, 2023). Advances in genomic technologies, such as DNA sequencing and gene editing, have transformed the field. DNA sequencing includes; Nextgeneration sequencing (NGS) (Marudamuthu et al., 2023), Ribonucleic acid sequencing (RNA-seq) (Upton et al., 2023), whereas, gene editing methods includes; Clustered Regularly Interspaced Short Palindromic Repeat- CRISPR associated protein 9 (CRISPR/ Cas9) (Q. Liu et al., 2021), Transcription activator-like effector nucleases (TALENS) Zinc-finger nucleases (ZNF) (Malzahn et al., 2017) and Oligonucleotide directed mutagenesis (ODM) (Ravikiran et al., 2025) as well as doubled haploids (Chen et al., 2024), molecular markers (Kumar et al., 2024) and mapping populations (Temesgen, 2021), offers potential solutions against such agricultural challenges (Hossain & Roslan, 2023). Genome sequencing techniques are applied to various fields of industrial biotechnology such as; strain development or improvement (Salazar-Cerezo et al., 2023), enzyme discovery (Zaparucha et al., 2018) and analysis of microbial community (Hosokawa et al., 2022). The potential to enhance microbial strains through the incorporation, deletion, or alteration of

5.1 Introduction Bioprocess engineering is becoming more advanced form of technology due to the many changes taking place in the field. The world is in dire need for creative, well thought out, and innovative approaches to problems associated with sustainable biomanufacturing, treatment customization, and the production of bio-based goods. One of the most notable advancements is the rise of synthetic biology, which allows for bioproduction at unprecedented levels where microbes could be modified for better bioproduction. More traditional approaches is strain improvement and metabolic engineering, which revolve around mutation-based processes and other forms of gene transfer technologies. The applications of new, non-traditional methods such as machine learning and artificial intelligence have completely revolutionized the way bioprocessing and its monitoring, as well as optimization, is done. On top of that, the drive towards sustainability has further motivated the use of green bioprocessing techniques which aim to minimize environmental harm. In parallel with these advances, regenerative medicine and gene treatment is opening doors to a new era of personalized medicine. In a world where change is constant, these accomplishments and innovations help us to take full advantage of what bioprocess engineering has to offer, as well as face the intricate challenges that the modern world encounters (Awasthi, 2022). In recent years, the field of biotechnology has undergone transformative and profound change that is marked by new breakthroughs and improvement which can affect several fields such as medicine, agriculture, and ecology. The emergence of tools like CRISPR-Cas9, synthetic biology, bioprinting, precision medicine and regenerative medicine are some of the new trends in biotechnology. These changes are recognizing the limits on the biological sciences and expanding them at the same time. Synthetic biology is another technology that includes the strategy and production of new genomic parts, related tools, and re-designs of traditional ones for valuable purposes. The scientists are modifying living organisms to perform useful functions like manufacturing biofuels or pharmaceuticals. Synthetic biology raises concerns about biosecurity and the unintentional release of artificial organisms into the environment, despite its enormous potential to address global challenges like renewable energy and sustainable resource usage (Jain, 2022). This article introduces advancements in bioprocess engineering with specific focus on advancements brought about by emergence of CRISPR. Bioprocess engineering is the application of process engineering principles to biological systems and has been a multidisciplinary field unto itself. Since its isolation as a groundbreaking genome-editing system, CRISPR has vastly improved the precision, efficiency, and scalability of both microbial and mammalian cell manipulation. This has resulted in numerous breakthroughs in pharmaceuticals, biofuels, and industrial enzymes, a major milestone for synthetic biology, and metabolic engineering (Jinek et al., 2012). The CRISPR revolution has changed

6.1 Introduction  Lignocellulosic Biomass (LCB) generated from the global consumption of fruit and vegetables rapidly generates approximately 140 billion metric tons of agricultural waste. Environmental pollution associated with these wastes, like land pollution, and increased greenhouse gas emissions, can be reduced through the bioconversion of wastes (renewable biomass resources) into value-added products in biorefineries (Babu et al., 2022). These value-added products synthesized for utilization in industries such as biofuels, biomaterials, or biochemicals are effective, reuseable, and enhance sustainability. These bioproducts are recuperated into useful biomolecules and oils for the food industries and dyes for the textile industries. The sustainability of these products can be acquired efficiently through costeffective pretreatments and bioprocess optimization methods (Haldar et al., 2020). The initial stage of value product chain development is the process modelling and optimizing, which, due to its complex nature and large scale, faces challenges in raw material availability, cost competitiveness, upscaling, and lack of technological maturity (Gerassimidou et al., 2021). Optimization of production processes like fermentation and chemical synthesis for manufacturing value-added products has become a focal point in research enhancement of biorefineries. There has been a revolution in the technology used in process designing and manufacturing practices in biorefineries. Advanced Artificial Intelligence (AI)-based approaches like Genetic Algorithm (GA), Fuzzy Inference System (FIS), and Artificial Neural Network (ANN) are being utilized along with conventional tools to optimize variables interacting in production processes. These techniques increase overall efficiency and enhance prediction accuracy and output of value-added products (Banerjee et al., 2025). Optimization of the bio processes enables higher productivity in parallel with lower environmental burdens. AI-based approaches integrated with conventional approaches need a large amount of experimental historical data to be successfully carried out (Arias et al., 2023). Advancement of biorefineries due to the rapid demand of value-added products as well as a solution to the greenhouse emissions, the integrated application of AI for process analysis needs to be understood. This chapter is a contribution towards upscaling the current knowledge base related to different approaches for the enhancement of production for value-added products. The chapter discusses the conventional techniques for value-added production and various optimization models. There is further discussion of advancements in the traditional approach using AI-based approaches and their features and benefits in biorefinery. Apart from different AI-based models, AI integrated with life cycle analysis for sustainability is also briefed. Finally, the chapter addresses current limitations in the field and outlines potential future directions for research and development.

7.1 Introduction  The global economy of today magnifies the extend and scope of product safety issues for a wide range of items, from food and medicine to consumer goods, medical devices, and automobiles (Marucheck et al., 2011). Industrial globalization has raised awareness of the many risks and vulnerabilities that goods face as they move through the supply chain, from design and sourcing to manufacturing, shipping, distribution, and ultimate sale to the end user (Milovanovi? et al., 2017). These challenges necessitate the implementation of reliable and sustainable methods for producing goods. This can be achieved via biorefineries, which may reduce supply chain disruptions while promoting environmentally friendly resource utilization. Biorefineries are usually referred to as first and second generation biorefineries with accordance to the raw material utilized. The former uses food crop as raw material wherein the latter utilizes lignocellulosic and agricultural residues as raw material for generation of value-added products (Guo et al., 2015). Additionally, algae biomass is utilized in the third generation biorefineries (Parajuli et al., 2015). However, among these three the first generation are the most established while the second and third are still developing at various stages due to technical and economic challenges (Gerssen-Gondelach et al., 2014). Globally, ethanol biorefinery processing U.S. corn and Brazilian sugarcane dominate production contributing 52% and 28%, respectively (Renewable Fuels Association, 2023). These biorefineries are further distinguished based on categories such as sugars, oils and syngas which serve as key intermediates in production of bioethanol, glycerol or lactic acid (Calvo-Flores and Martin- Martinez, 2022). Additionally, these biorefineries utilizes feedstock and residues which can be derived from forest, sugar crops and oil crops. The processing technologies employing for utilization of such raw material are fermentation, gasification and pyrolysis. This configuration of biorefinery determines the integration and systematic pathway from raw feedstock to final product (Aristizábal?Marulanda et al., 2019). Furthermore, both the supply chain and value chain are significant part of biorefineries. The value chain stands for the value-oriented aspect of the process which is often derived by customer demand and include product innovation, marketing and value development (Sharma et al., 2013). In contrast, the supply chain focuses on the logistical flow of materials, encompassing the movement of raw materials from suppliers to end consumers. However, certain stages of supply chain overlap with biorefinery systems such as separate pretreatment and fermentation facilities (Sutduean et al., 2019). The design of biorefinery system involves a structural decision process at strategic, tactical and operational levels. These decisions are made with respect to various biohazards at each level. The macro-level strategic decisions, address long-term risk mitigating strategies to ensure overall safety (Chaturvedi et al., 2020). Once the high-level decisions are established, tactical and operational decisions are implemented. The tactical decisions focus on implementing safety measures with processes such as handling hazardous materials and optimizing biorefinery. While

8.1 Introduction The increasing rate of waste generation due to consumption patterns has emerged as a significant environmental management challenge. The valorization of this waste hold great potential in production of bio-based products, provided they meet the multi-criteria of environmental sustainability and economic viability (Dragone et al., 2020). Within this framework, the advancement of biorefineries is considered to be a valuable strategy. These transform lignocellulosic biomass to valuable bio-based products including biofuels and biobased chemicals which promote circular economy (Mariana et al., 2021). The incorporation of biorefineries into the market value chain necessitates the establishment of production framework which aims high recovery and generation of value-added products from biomass (Meramo- Hurtado and González-Delgado, 2019). However, the upscaling of these biorefineries faces various challenges such as process efficiency and technological advancement. Additionally, the largescale operation of biorefineries requires consistent and stable biomass supply. Addressing these challenges require technological advancements and policy framework (Makepa and Chihobo, 2024). These could ensure the economic viability and profitability of biorefineries which is essential for their successful implementation in long-term sustainability goals (Arias et al., 2023). In this context, the early-stage evaluation comprises production capacity, market demand, technological limitations and environmental impact. 8.2 Challenges in Upscaling of Biorefineries For fulfillment of the global demand for bio-based products it is important to upscale biorefineries from laboratory to industrial scale. Biorefineries are closely aligned with the United Nations Sustainable Development Goals (SDGs), as several SDGs emphasize the efficient and circular utilization of resources to promote sustainability. Additionally, SDSs promotes adoption of practices which encourage climate change mitigation, economic growth, societal well-being and advancements in research and innovations (Lange et al., 2021). However, the establishment of biorefineries faces various technical challenges similar to those encountered in conventional refinery plants. These challenges arise primarily due to the large volume of biomass required for processing to achieve industrial scale production of bioproducts (Ahmed et al., 2023).

9.1 Introduction  Biochemical technology focuses on developing sustainable conversion of renewable materials like lignocellulosic wastes into value-added products with reduced emissions. The utilization of integrated bioprocess engineering for transforming carbon sources into diverse biotechnological products via microbial cells is an emerging field (Pais et al., 2016). Bioprocess engineering is a collection of thermodynamics, process simulation, bio-separations, microbiology, bioprocess kinetics, and systems engineering aiming for biomass conversion, biocatalysis-driven processing methods, and microbial processes. These processes can range from microscale to large-scale designs and systems (Liu et al., 2016) and generate value-added products such as biofuels like bioethanol, biodiesel, and biogas (Patel et al., 2022); in pharmaceuticals (Tsopanoglou et al., 2021), probiotics (Lei et al., 2024), textiles (Bahtiyar et al., 2021), bioplastics, and biopolymers (Pathom-aree et al., 2024). Reactor design, fermentation, kinetics, and optimization are integral to bioprocess engineering. Not always but the pretreatment methods for lignocellulosic waste and its improvement should be considered for an optimized bioprocess engineering as they are the carbon source during fermentation. Pretreatment methods are physical, chemical, biological, or hybrid of any three treatments used to acquire desired products. Cost-effective and relatively slow biological pretreatment needs microorganisms to convert lignocellulosic waste into useful desired products (Abo et al., 2019). Chemical pretreatment utilizes harsh chemicals to increase biomass degradation, porosity, and solid separation but unfortunately are rarely used due to the harmful effects (Arora et al., 2018). Physical pretreatment methods utilize mechanical pressure to break down biomass but are expensive and irregular. Pretreatment for source and strain acquisition is the initial step of upstream bioprocessing followed by reaction conditions and bioreactor design (John et al., 2020). The primary part of any upstream process is to produce the desired product like enzyme followed downstream processing such as purification of the value-added product. The value-added products are developed via fermentation in equipment known as Bioreactors. Different bioreactors being utilized recently in bioprocess engineering and biorefinery are membrane bioreactors that offer high productivity, low environmental impact, and high flexibility (Akkoyunlu et al., 2024); airlift bioreactors (Hernández- Acevedo et al., 2024); fixed bed/packed bed and fluidized bed bioreactors are being utilized for their potential of reduced expenses, simpler design, enhanced temperature regulation and homogenous mixing (Srivastava et al., 2022) (Figure 10.1). Despite their high production and safety, the traditional large-scale stainless-steel vessels batch-wise are often expensive, inflexible, and have long construction times. As an alternative to the costly route, single-use equipment was introduced. Since they are pre-sterilized and are readily accessible to purchase as needed, this disposal method offers great flexibility and lower investment costs by eliminating the need for sterilization. However, there is high waste generation leading to a dent in sustainability and circular bioeconomy (Frank, 2018). The transformative step of the production system shifting from batchwise to continuous production has ultimately led to a decreased environmental footprint and prices and increased product quality and yield (Gerstweiler et al., 2021). The cost of bioprocess engineering (upstream and downstream) is dependent on factors such as types of feedstocks, fermentation types like submerged and solid-state, product types, and microbe or enzyme types (Kumar