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Artificial intelligence ,robotics and cfd by sneha gaurkar

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Artificial intelligence ,robotics and cfd by sneha gaurkar

  1. 1. A R T I F I C I A L I N T E L L I G E N C E , R O B O T I C S A N D C O M P U T A T I O N A L F L U I D D Y N A M I C S - Sneha Gaurkar M.Pharm II sem Department of Pharmaceutics Smt. Kishoritai Bhoyar College Of Pharmacy, kamptee
  2. 2. Contents :  Artificial Intelligence Introduction Applications Advantages Risks Challenges Future aspects  Robotics Introduction Types Advantages Challenges  Computational fluid dynamics  Introduction  Applications  Advantages  Disadvantges  Current challenges and future aspects 2
  3. 3. A R T I F I C I A L I N T E L L I G E N C E 3
  4. 4. ARTIFICIAL INTELLIGENCE Introduction to Artificial Intelligence :  According to the father of Artificial Intelligence, John McCarthy, it is, “The science and engineering of making intelligent machines, especially intelligent computer programs”.  Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think.  AI is accomplished by studying how human brain thinks, and how humans learn, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis of developing intelligent software and systems. 4
  5. 5. Also, intelligence distinguish us from everything in the world. As it has the ability to understand, apply knowledge.  Also, improve skills that played a significant role in our evolution. We can define AI as the area of computer science.  Further, they deal with the ways in which computers can be made. As they made to perform cognitive functions ascribed to humans. 5 Cont…
  6. 6. Definition Of Artificial Intelligence :  AI may be defined in simple words that “It is the study of ideas which enable computers to do the things that make people seem intelligent”.  Artificial intelligence refers to the ability of a computer or a computer enabled robotic system to process information and produce outcomes in a manner similar to the thought process of humans in learning , decision making and solving problems.  By extension the goal of AI system is to develop system capable of tacking complex problems in ways similar to human logic and reasoning.  AI is the branch of science which deals with the helping machines to find the solutions of the complex problems in a more human like fashion. 6
  7. 7. Artificial intelligence Machine learning Deep learning 7
  8. 8. Simple Neural Network Deep Neural Network 8 For example…
  9. 9. Properties Of AI Software :  A software generates new molecular structures by combining properties of existing drugs.  Automatic chemical design helps drug discovery for a faster & accurate manner.  A treatment method from the ground up using a deep learning neural network.  A system to use historical , biological & chemical data to imagine novel molecules with the potential to fight major diseases. 9
  10. 10. Applications of artificial intelligence (AI) in different subfields of the pharmaceutical industry 10
  11. 11. AI Techniques In Use For Drug Discovery Deep learning technique known as a generative adversarial network (GAN) by Baltimore based company, Insilico Medicine.  GPU (graphics processing unit)-accelerated deep learning to target cancer and age-related illnesses by above organization.  Benevolent Bio’s deep learning software, powered by the NVIDIA DGX-1 AI supercomputer (it ingests & analyzes the information to find connections and propose drug candidates). 11
  12. 12. AI In Drug Discovery 12
  13. 13. Examples of AI tools used in drug discovery Tools Details Website URL DeepChem MLP model that uses a python-based AI system to find a suitable candidate in drug discovery https://github.com/d eepchem/deepchem DeepTox Software that predicts the toxicity of total of 12 000 drugs www.bioinf.jku.at/res earch/DeepTox DeepNeuralNetQSAR Python-based system driven by computational tools that aid detection of the molecular activity of compounds https://github.com/M erck/DeepNeuralNet- QSAR 13
  14. 14. Cont… 14 Tools Details Website URL ORGANIC A molecular generation tool that helps to create molecules with desired properties https://github.com/as puru-guzik- group/ORGANIC PotentialNet Uses NNs to predict binding affinity of ligands https://pubs.acs.org /doi/full/10.1021/ac scentsci.8b00507 Neural graph fingerprint Helps to predict properties of novel molecules https://github.com/ HIPS/neural- fingerprint AlphaFold Predicts 3D structures of proteins https://deepmind.co m/blog/alphafold
  15. 15. AI in clinical trial design  Preclinical discovery of molecules as well as predicting lead compounds before the start of clinical trials by using other aspects of AI, such as predictive ML and other reasoning techniques, help in the early prediction of lead molecules that would pass clinical trials with consideration of the selected patient population.  Drop out of patients from clinical trials accounts for the failure of 30% of the clinical trials, creating additional recruiting requirements for the completion of the trial, leading to a wastage of time and money. This can be avoided by close monitoring of the patients and helping them follow the desired protocol of the clinical trial  Mobile software was developed by AiCure that monitored regular medication intake by patients with schizophrenia in a Phase II trial, which increased the adherence rate of patients by 25%, ensuring successful completion of the clinical trial 15
  16. 16. AI in pharmaceutical product management AI in market positioning Market positioning is the process of creating an identity of the product in the market to attract consumers to buy them, making it an essential element in almost all business strategies for companies to establish their own unique identity With the help of technology and e-commerce as a platform, it has become easier for companies to get a natural recognition of their brand in the public domain. Other techniques, such as statistical analysis methods, particle swarm optimization algorithms (proposed by Eberhart and Kennedy in 1995) in combination with NNs, provided a better idea about markets. They can help decide the marketing strategy for the product based on accurate consumer-demand prediction 16
  17. 17. Cont… AI in market prediction and analysis  The advances in digital technologies, referred to as the ‘Fourth industrial revolution’, is helping innovative digitalized marketing via a multicriteria decision-making approach, which collects and analyzes statistical and mathematical data and implements human inferences to make AI-based decision-making models explore new marketing methodology  AI-based software engages consumers and creates awareness among physicians by displaying advertisements directing them to the product site by just a click.  In addition, these methods use natural language-processing tools to analyze keywords entered by customers and relate them to the probability of purchasing the product.  Pharmaceutical companies are also introducing their online applications such as 1 mg, Medline, Netmeds, and Ask Apollo, to fulfill the unmet needs of the patients. 17
  18. 18. Advantages of AI a. Error Reduction  We use artificial intelligence in most of the cases. As this helps us in reducing the risk.  Also, increases the chance of reaching accuracy with the greater degree of precision. b. Difficult Exploration  In mining, we use artificial intelligence and science of robotics. Also, other fuel exploration processes.  Moreover, we use complex machines for exploring the ocean. Hence, overcoming the ocean limitation. 18
  19. 19. Cont… c. Daily Application  As we know that computed methods and learning have become common place in daily life.  Financial institutions and banking institutions are widely using AI. That is to organize and manage data.  Also, AI is used in the detection of fraud users in a smart card based system. d. Digital Assistants  “Avatars” are used by highly advanced organizations. That are digital assistants.  Also, they can interact with the users. Hence. They are saving human needs of resources.  As we can say that the emotions are associated with mood. That they can cloud judgment and affect human efficiency. Moreover, completely ruled out for machine intelligence. 19
  20. 20. Cont… e. No breaks  Machines do not require frequent breaks and refreshments for humans. As machines are programmed for long hours.  Also, they can continuously perform without getting bored. f. Increase Work Efficiency  For a particular repetitive task, AI-powered machines are great with amazing efficiency.  Best is they remove human errors from their tasks to achieve accurate results. Reduce cost of training and operation.  Deep Learning and neural networks algorithms used in AI to learn new things like humans do.  Also, this way they eliminate the need to write new code every time. 20
  21. 21. Risks of Artificial Intelligence a. High Cost  Its creation requires huge costs as they are very complex machines. Also, repair and maintenance require huge costs. b. No Replicating Humans  As intelligence is believed to be a gift of nature. An ethical argument continues, whether human intelligence is to be replicated or not. c. Lesser Jobs  As we are aware that machines do routine and repeatable tasks much better than humans.  Moreover, machines are used of instead of humans. As to increase their profitability in businesses. 21
  22. 22. Cont… d. Lack of Personal Connections  We can’t rely too much on these machines for educational oversights. That hurt learners more than help. e. Addiction  As we rely on machines to make everyday tasks more efficient we use machines. f. Efficient Decision Making  As we know computers are getting smarter every day.  Also, they are demonstrating not only an ability to learn but to teach other computers. 22
  23. 23. Challenges to the adoption of Artificial Intelligence (AI) in Pharma 23 The unfamiliarity of the technology Overfitting or underfitting Low accuracy of the training data Breaking down data silos and streamlining electronic records Lack of proper IT infrastructure Hesitant to change Data quality, governance, security, and interoperability
  24. 24. Cont… A. The unfamiliarity of the technology: For many pharma companies, AI still seems like a “black box” owing to its newness and esoteric nature. B. Lack of proper IT infrastructure: Most IT applications and infrastructure currently in use are not developed or designed with AI in mind. Even worse, pharma companies have to spend lots of money to upgrade their IT system. C. Breaking down data silos and streamlining electronic records: Much of the data is in a free text format, and the data management is messy and unorganized across the heterogeneous databases. This means that pharma companies have to go above and beyond to collate and put this data into a standard form that can be analyzed. D. Low accuracy of the training data: Even though algorithms have a higher threshold for minimizing errors, there are still some categorical errors from training sets. 24
  25. 25. E. Overfitting or underfitting: With algorithm prediction, there is a concern with overfitting or underfitting. Overfitting means when a model consists of lower quality information/technique but generates higher quality performance. Underfitting models fail to recognize the underlying trend in the datasets and generalize the new data. Both result in inaccurate results. F. Data quality, governance, security, and interoperability: Issues around data will always be at the heart of successfully promoting AI solutions. Healthcare is the least digitized sector, which needs to take a systematic approach to develop common data standards and processes to maximize the value of existing data. Healthcare providers and AI companies need to put in place robust data governance, ensure interoperability and standards for data formats, enhance data security and bring clarity to consent over data sharing. 25 Cont…
  26. 26. G. The need for transparent algorithms to meet drug development regulations: Transparency in health care is quite a task given the complexity of the processes involving artificial intelligence. H. Hesitant to change: Pharma companies are known to be traditional and resistant to change. 26 Cont…
  27. 27. Future prospects  AI technologies are now used at every stage in pharmaceutical R&D, as is evidenced by the numerous high-value deals between ‘big pharma’ and providers of AI technologies.  As a recent example, Eversheds Sutherland advised global biopharmaceutical company AstraZeneca on its long-term collaboration with BenevolentAI to use AI and machine learning for the discovery and development of potential new treatments for chronic kidney disease (CKD) and idiopathic pulmonary fibrosis (IPF).  Further to this, even if the prospect of machines inventing medicines is some way off, the use of AI to improve efficiency and speed up drug discovery is a realistic prospect today. 27
  28. 28. Cont…  AI technologies may also be utilised in areas of manufacturing which are amenable to predictive modelling and predictive support, such as process optimisation.  The use of such AI tools within smart factories and smart manufacturing is enabling less down-time in the manufacturing process and driving stronger results. And AI tools are also beginning to be deployed on a more wide-spread basis within the ‘back office’ such as within accounting technology. As AI tools and computing power become more accessible and affordable, as with any technology, it will become very much part of the process within pharma and manufacturing – the future will be ‘AI-enabled’. 28
  29. 29. 29 R O B O T I C S
  30. 30. Introduction  The International Organization for Standardization gives a definition of robot in ISO 8373: "An automatically controlled, reprogrammable, multipurpose, manipulator programmable in three or more axes, which may be either fixed in place or mobile for use in industrial automation applications.“  Robotics is a domain in artificial intelligence that deals with the study of creating intelligent and efficient robots.  Robots are the artificial agents acting in real world environment. 30
  31. 31. Types of Robots • There are three types of industrial robots most commonly used in pharmaceutical manufacturing.  In their simplest form, Cartesian robots consist of two linear slides placed at 90-degree angles to each other, with a motorized unit that moves horizontally along the slides in the axis (z), which moves up and down in the vertical plane.  The quill holds the robot’s end-effector, such as a gripper. A fourth axis (t, or theta) allows the quill to rotate in the horizontal plane. 31 1. Cartesian
  32. 32. Cont…  The chief advantage of Cartesian robots is their low cost, although their restricted range of motion limits their usage.  They are often incorporated into automation subsystems or machines dedicated to a single purpose, such as assay testing 32
  33. 33. 2. SCARA Robots SCARA stands for “selective compliance articulated robot arm.”  This refers to the fact that a SCARA’s arm segments, or links, are “compliant,” that is, they can move freely, but only in a single geometrical plane.  Most SCARAs have four axes. Even though three- and five-axis SCARAs are also found, the terms “SCARA” and “four axis robot” are often used interchangeably to refer to a four-axis SCARA.  The first two links of a SCARA swivel left and right around the first two axes in the horizontal plane. The third link is the quill, which moves up and down in the vertical plane along the third axis.  The quill also rotates horizontally in the fourth axis, but cannot tilt at a vertical angle.  Some SCARAs have a metal shaft that is actually a hollow air- balance cylinder. 33
  34. 34.  The function of this cylinder is to counterbalance the weight of the end- effector and payload and thus reduce settling time—the time in which robot has to wait after it moves to a given point before it can carry out its next movement.  Faster settling times result in faster cycle times.  The unique design of SCARAs gives them a high degree of rigidity, which in turn allows them to move very fast and with precise repeatability.  SCARAs excel at high-speed pick-and- place than other material-handling tasks. 34
  35. 35. 3. Articulated Robots  Articulated robots not only have more joints than SCARAs, they have both horizontal and vertical joints, giving them increased freedom of movement. Whereas a Cartesian robot has a cube- shaped work envelope and a SCARA has a cylindrically shaped one, the work envelope of an articulated robot is spherical.  With their greater flexibility of movement, articulated robots can perform almost any task that can be performed by a human arm and hand.  The most common articulated robots have six axes.  The first link rotates in the horizontal plane like a SCARA, while the second two links rotate in the vertical plane. 35
  36. 36.  In addition, six-axis articulated robots have a vertically rotating “forearm” and vertically rotating “wrist” joint, which let them perform many of the same types of movements as a human forearm and wrist.  The forearm and wrist joint of six-axis articulated robots allow them to pick up an object no matter how it is oriented off the horizontal plane, then place it at any angle of approach that might be required.  They also allow the robot to perform many other tasks that would otherwise call for the dexterity of a human operator. 36 Comparison between robot and human movement. (Image: Kawasaki)
  37. 37. NANO-ROBOTS  Nano-robots are so tiny machines that they can traverse the human body very easily.  When a nano-robot enters into the body of a patient would seek for infected cells and would repair them without causing any damage to the healthy cells.  The nano-robot will remain outside the cell while the nano- manipulators will penetrate into targeted or damaged cell thus avoiding any possibility of causing damage to the intracellular skeleton. 37
  38. 38.  Thus these nano-robots when enter into human bloodstream provide cell surgery and extreme life prolongation.  Each nano-robot by itself will have limited capabilities, but the coordinated effort of a multitude will produce the desired system level results.  Coordination is needed across the board for communication, sensing, and acting and poses a major research challenge. 38
  39. 39. Determining the Type of Robot Needed  The first step in automating a process with a robot is to establish the process parameters, including (1) The required type and size of end-effector, or end-of-arm tooling (2) Cycle time (3) Repeatability (4) Reach (5) Payload capacity  Taken together, these will usually determine whether a Cartesian, SCARA or articulated robot is necessary.  Another essential consideration is the environment in which the robot will be operating. 39
  40. 40. 40 Accuracy Tirelessness Return on investment Reliability Affordability Speed Advantages of Pharmaceutical Robots And Production Quality Flexibility Safety Reduced chances of contamination Work continuously in any environment And Savings Advantages
  41. 41. Challenges 1. Expense: The initial investment of robots is significant, especially when business owners are limiting their purchases to new robotic equipment. The cost of automation should be calculated in light of a business' greater financial budget. Regular maintenance needs can have a financial toll as well. 2. Dangers and fears: Although current robots are not believed to have developed to the stage where they pose any threat or danger to society, fears and concerns about robots have been repeatedly expressed in a wide range of books and films. The principal theme is the robots' intelligence and ability to act could exceed that of humans, that they could develop a conscience and a motivation to take over or destroy the human race. 41
  42. 42. 3. Expertise: Employees will require training in programming and interacting with the new robotic equipment. This normally takes time and financial output. 4. Return on investment (ROI): Incorporating industrial robots does not guarantee results. Without planning, companies can have difficulty achieving their goals. 5. Safety: Robots may protect workers from some hazards, but in the meantime, their very presence can create other safety problems. These new dangers must be taken into consideration. 42
  43. 43. 43 C O M P U T A T I O N A L F L U I D D Y N A M I C S
  44. 44. Introduction  CFD is a branch of fluid mechanics that applies numerical methods for solution of physical problems involving fluid flows.  CFD is now recognized to be an important part of the computer-based engineering design tools and is used extensively today in many industries.  In general, computational methods can be used to analyze various design and process parameters, while, at the same time, reducing the involved experimental work and associated costs.  Computational modeling has been found useful in the study of various pharmaceutical unit operations, for example, mixing and dissolution 44
  45. 45. Definition  Computational fluid dynamics (CFD) is a branch of physics that deals with the study of the mechanics of fluid: liquid, plasmas and gasses and forces acting on them.  CFG is based on Navier-Stroke equations that describe how pressure, velocity, density and temperature of a moving fluid are related. 45
  46. 46. APPLICATION OF CFD IN PHARMACEUTICS •The application of CFD to a few key unit operations and processes in the pharmaceutical industry 46 CFD for mixing CFD for solids handling CFD for separation CFD for dryers CFD for packaging CFD for energy generation and energy-transfer devices
  47. 47.  CFD methods can be applied to examine the performance of static mixers and to predict the degree of mixing achieved, thus indicating whether more mixing elements are required shows surface mesh and blade orientation for a Kenics mixer depicts the mass fraction concentration of the two species being mixed.  The degree of mixing is shown as the color proceeds from distinct inlet streams (red and blue) to the fully mixed outlet stream (green).  A CFD solution can be used to derive the pressure drop, hence the power required. 47 CFD for mixing Example: Mixing of Powders
  48. 48. CFD techniques can be applied to analyze such flows and minimize or eliminate the risk of erosion.  CFD also can be applied to analyze the unsteady and chaotic flow behavior in fluidized beds.  Simulation of such a flow field requires unsteady flow calculations and small time increments.  As a result, performing calculations can take an extensive amount of time.  Simulations of gas–solid flows in complex three-dimensional reactors can take months of computational time and are not practically feasible 48 CFD for solids handling
  49. 49. CFD techniques are used for analyzing separation devices such as cyclones and scrubbers.  The following example incorporates CFD methods to optimize and predict performance of an existing cyclone design.  CFD solutions depict particle paths for various particle sizes.  In this example, CFD techniques were used to perform what-if analysis for optimization of the design. The performance computed with CFD closely matched that observed in physical testing wherein 90% of 10ưm particles were removed, but only 10% of 1ưm particles were separated from the air stream. 49 CFD for separation
  50. 50.  We used CFD to analyze the performance of an industrial spray dryer before making major structural changes to the dryer.  This strategy minimizes the risk of lost profit during changeover, especially if the improvement does not materialize.  CFD was applied to examine configuration changes, thus minimizing risk and avoiding unnecessary downtime during testing shows the velocity distribution (skewed flow).  This flow is a result of uneven pressure distribution in the air dispersing head.  CFD models were applied to determine optimum equipment configuration and process settings. 50 CFD for dryers
  51. 51.  CFD can be applied to conduct virtual experiments before changes are made to the filling lines or to the package geometry.  This method allows a wide range of conditions to be tested and leads to an optimized filling process, depicts the filling of a container.  The figures shown are typical of solution results that are used to optimize filling processes to increase throughput and reduce foaming. (a)filling process, liquid surface location, strong splash; (b) filling process, liquid surface location, no splash. 51 CFD for packaging a) b)
  52. 52.  CFD techniques can be applied to analyze thermal and flow fields within such devices.  CFD modeling methods also can be applied to gain insight into flame characteristics.  Maintaining flame stability and burner efficiency is very critical to the proper functioning of a process heater, power plant, or furnace. Flame length, shape, and size can influence the process.  If the flame is too long, then it can impinge on critical regions of the apparatus and cause thermal damage.  If the flame is too short, then it may wear out the burner tip.  Replacing the burner or associated apparatus results in downtime and loss of product revenue. 52 CFD for energy generation and energy-transfer devices
  53. 53. Advantages Of CFD  A great time reduction and cost reduction in new designs.  There is a possibility to analyze different problem whose experiments are very difficult and dangerous.  The CFD techniques offer the capacity of studying system under conditions over its limits.  The level of detail is practically unlimited.  The product gets added value.  The possibility to generate different graph permits to understand the features of the result. This encourages buying a new product.  Hi-Tech CFD is a computer aided engineering company which provides total solutions to engineering problems in the field of Computational Fluid Dynamics (CFD),Computational Electromagnetic, Computational Structural Mechanics, Dynamics and Controls. 53
  54. 54. Disadvantages Of CFD  Accuracy in the result is doubted i.e. in certain situations we will not obtain successful result.  It is necessary to simplify mathematically the phenomenon to facilitate calculus. If the simplification has been good the result will be more accurate.  There are several incomplete models to describe the turbulence, multiphase phenomenon, and other difficult problems.  Untrained user of CFD has the tendency to believe that the output of the pc is always true 54
  55. 55. Softwares of CFD: CFD Simulation :CFD simulation software helps to predict the impact of fluid flows on a given product throughout design and manufacturing as well as during end use. Passage® software is a collection of finite element programs solving complex flow, heat transfer and other related problems in product design and manufacturing. PASSAGE Discrete Element Method (DEM) Software is for predicting the flow particles under a wide variety of forces. It can be used alone or together with our PASSAGE/FLOW Software. DEM and FLOW CFD Software 55
  56. 56. Current Challenges/ Future Aspect  The integration of CFD methods can shorten product-process development cycles, optimize existing processes, reduce energy requirements, and lead to the efficient design of new products and processes.  Unit operations in the pharmaceutical industry handle large amounts of fluid. As a result, small increments in efficiency, such as those created by implementing CFD solutions, can lead to significant product cost savings.  Key processes in the pharmaceutical industry can be improved with CFD techniques.  The aerospace and automobile industries already have integrated CFD methods into their design process.  The chemical process and the pharmaceutical industries now are beginning to integrate this technology.  The full potential for process improvements using CFD solutions is yet to be realized 56
  57. 57. References: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7577280/ https://www.slideshare.net/SyedImran151/pharmaceutical- robotics SHEPARD JEFF ,Robot axes, drive safety, and power architectures MARCH 8, 2021 https://www.microcontrollertips.com/robot-axes- drive-safety-and-power-architectures/ https://www.slideshare.net/PRAJAKTASAWANT33/computer-aided- drug-development-249384130 57
  58. 58. 58 References: Challenges to the adoption of Artificial Intelligence (AI) in Pharma By Editorial -April 23, 2021 https://roboticsbiz.com/challenges-to- the-adoption-of-artificial-intelligence-ai-in-pharma/ https://www.slideshare.net/SyedImran151/pharmaceutical- robotics https://www.slideshare.net/PrasathP13/artificial-intelligence- 138582081 http://www.technalysis.com/pharmaceutical_industry.aspx https://alfrescostaticfiles.s3.amazonaws.com/alfresco_images/ph arma/2014/08/22/86fd0a7c-40ff-47ef-8e11-22ac54892744/article- 9676.pdf
  59. 59. THANK YOU !!! Skb cop