CAFE4DM. Enabling smart factories in the process sector: delivering Industry 4.0 for efficient manufacturing of formulated products

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1 Enabling smart factories in the process sector: delivering Industry 4.0 for efficient manufacturing of formulated products Prof. Philip Martin School of Chemical Engineering and Analytical Science University of Manchester CAFE4DM Centre in Advanced Fluid Engineering for Digital Manufacturing

2 Formulated Products Examples of formulated products: Food Pharmaceuticals Personal care Lubricants Coatings and paints Inks Paper Agrochemicals Emulsions, (droplets of immiscible liquids in a continuous phase)...

3 Talk Outline Introduction to formulated products manufacturing Motivation to change Smart factories: virtual process engineering A virtual process twin Process analytics Business acceptance Conclusions

4 THE CHANGING FACE OF INNOVATION AND THE NEED FOR SPEED Traditional routes to successful business in a global, volatile, uncertain environment are losing their robustness. The only sure fire approach is to outpace all the competition In-silico approaches offer one of the best routes to accelerate right through the innovation chain and enable global scale. Best in class fast moving consumer product companies introduce innovations seven months faster: resulting in 60% higher sales in first year. 22 Months 15 Months 7 Months Average Best *BCG Report Speed to Win April 2012

5 HOW CAN WE ACCELERATE OUR FORMULATION AND MANUFACTURING JOURNEY No. of FULL Formulations per person per day In silico 1000 s Automated, 25 Manual, standardised, 5-7 Manual, non standard 2-3

6 Speed of innovation, optimise product performance Do more at smaller scale Better mapping of process-> microstructure -> functionality & sensory Better scale-up; not all physical processes scale in the same way Variables not important at small scale become important at big scale. Improve margins Vision Virtual Process Engineering Transform the speed, accuracy and sustainability of product innovations through the power of simulation and digital Raw material flexibility & improved robustness to variability Increased factory capacity around the world

7 Product Life-cycle time Design new formulation optimise Scale-up optimise Manufacture Digital design space Digital process model Digital model predictive control Virtual process engineering

8 Formulated products manufacturing Raw materials (Ingredients) Mix Formulation (recipe) Product Currently: if we follow the recipe, the product quality will be ok

9 Following recipes Standard Victoria Sandwich Cake 4 oz. butter or margarine 4 oz. sugar 2 eggs 4 ox. flour ½ tsp. baking powder A little milk Jam Castor sugar Cream fat and sugar until light and creamy, then beat in the eggs one at a time. Add the sieved flour and baking powder, together with a little milk to give a soft dropping consistency. Put into two prepared sandwich tins and bake in a moderate oven (375 F for mins. Cool on a rack, and when cold sandwich together with jam. Dust lightly with castor sugar

10 Smart Factory Virtual Process Engineering Raw materials Mix Formulation (recipe) Product Data Quality assurance Data Process analytics Data Product Properties (QA) Digital process model Model predictive control Combine input and product quality data to drive process optimisation Real-time prediction of process parameters and ability to detect process abnormalities in real time

11 Viscosity of 5% product (Pa s) ACCELERATING MANUFACTURING: We need to understand how PROCESS INFLUENCES MICROSTRUCTURE Stress (Pa) Same formulation but viscosity differs by order of magnitude - Process design cannot be ignored during product design How can we predict properties (rheology) and structure through manufacturing process?

12 Modelling of complex fluids Model microstructure Dissipative particulate dynamics (DPD) Integrate DPD with CFD via constitutive equations Fig. 3 Example of methods to identify surfactant cluster size from DPD simulations. Development of continuum approaches Viscoelastic computational fluid dynamics (CFD) model Fig. 2 Turbulent structures in

13 Challenges: (loss of) scale up Lab scale Industrial scale Scale up historically relied on the scale similarity of turbulence Turbulent eddies look the same regardless of magnification* The turbulence is Characterised by a Reynolds number Traditional scale up = match Reynolds numbers

14 Process Analytics Accurate, real-time, in-line measurements at pilot and manufacturing scale. Sophisticated process analysers and low cost sensors Validation of virtual process models Measure critical parameters in manufacturing process Big data Low Flow High Flow Electrical Resistance Tomography (ERT)

15 CAFE4DM Centre in Advanced Fluid Engineering for Digital Manufacturing

16 Strategy Simulations/ Modelling Experimentation Model Optimisation Determine the best Parameters Process Model Database Simulation of Processes Incorporation of process analytics for validation and online monitoring Incorporation of innovation/behavioural change management

17 Innovation Management and Behavioural Change How to promote behavioural change within this new digital environment? How can leaders can use digital information to make better, quicker decisions? How to use data visualisation techniques and data mining to enhance knowledge sharing? How to enhance the adoption of these technologies and facilitate the radical change in organisation and process of innovation within the company which is required for this new digital workflow?

18 Conclusions The application of Industry 4.0 concepts through virtual process engineering will accelerate the design of new formulations with better energy and material usage, reduced waste and the minimisation of process downtime. Modelling complex behaviour at different scales now becoming possible so that a digital process twin can be developed with confidence CAFE4DM@manchester.ac.uk