Implementing AI Solutions in Bioprocess Engineering
Expert-defined terms from the Professional Certificate in AI Applications in Bioprocess Engineering course at London School of International Business. Free to read, free to share, paired with a globally recognised certification pathway.
Artificial Intelligence (AI) #
the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction.
Bioprocess Engineering #
the application of engineering principles to biological systems, including the design, optimization, and control of bioprocesses. Bioprocesses involve the conversion of raw materials into valuable products using biological catalysts, such as enzymes or cells.
Big Data #
large and complex datasets that cannot be easily managed, processed, or analyzed using traditional data processing techniques. Big data often requires advanced analytics tools, such as AI and machine learning, to extract meaningful insights and knowledge.
Computational Fluid Dynamics (CFD) #
a branch of fluid mechanics that uses numerical methods and algorithms to solve and analyze problems related to fluid flow and heat transfer. CFD simulations can help predict the behavior of bioprocesses and optimize their performance.
Deep Learning #
a subset of machine learning that uses artificial neural networks with many layers (deep structures) to learn and represent complex patterns and relationships in data. Deep learning algorithms can automatically extract features from raw data and make accurate predictions and decisions.
Genetic Algorithms #
optimization algorithms inspired by the process of natural selection and genetics. Genetic algorithms use a population of candidate solutions and apply genetic operators, such as selection, crossover, and mutation, to evolve better solutions over time.
Internet of Things (IoT) #
a network of interconnected physical devices, vehicles, buildings, and other objects that are embedded with sensors, software, and other technologies to collect and exchange data. IoT can provide real-time monitoring and control of bioprocesses and improve their efficiency and productivity.
Machine Learning #
a subfield of AI that focuses on the development of algorithms and statistical models that enable computers to learn and improve from data without explicit programming. Machine learning can be supervised, unsupervised, or reinforcement learning.
Process Analytical Technology (PAT) #
a framework for designing, analyzing, and controlling bioprocesses using real-time measurements and data. PAT can help ensure product quality, increase process efficiency, and reduce development time and costs.
Reinforcement Learning #
a type of machine learning that involves an agent interacting with an environment and learning to make decisions based on rewards and penalties. Reinforcement learning can be used to optimize bioprocess control strategies and improve product yields and quality.
Robotic Process Automation (RPA) #
the use of software robots or automated agents to perform routine, repetitive, and high-volume tasks. RPA can help reduce human error, increase process efficiency, and free up human resources for more value-added activities.
Supervised Learning #
a type of machine learning that involves training a model on labeled data, where the input data and the corresponding output or target variable are known. Supervised learning can be used for classification, regression, and other predictive tasks.
Unsupervised Learning #
a type of machine learning that involves training a model on unlabeled data, where the input data have no corresponding output or target variable. Unsupervised learning can be used for clustering, dimensionality reduction, and other exploratory tasks.
Virtual Sensors #
software models or algorithms that estimate process variables or properties based on measured data and physical principles. Virtual sensors can provide real-time monitoring and control of bioprocesses and reduce the need for expensive or invasive sensors.