Process and equipment design optimising product properties and attributes

Classically, when products have been developed at the bench, process engineers will search for equipment to manufacture the product at large scale. More than often, this search is constraint to the existing equipment base, or a catalog search for standard equipment. It is then not surprising, that the product manufactured at large scale, either deviates significantly from the intended product, and/or the incurred costs to manufacture the product are much higher than anticipated because the equipment has not been designed for this product, or product range. This paper describes the combined design of an extruder equipment and the operating conditions to process ice cream with desired product attributes

Modeling and Identification of Flexible Wind Turbines and A Factorizational Approach to Robust Control Design

Mechanical Maritime and Materials Engineerin

Process and equipment design optimising product properties and attributes

Classically, when products have been developed at the bench, process engineers will search for equipment to manufacture the product at large scale. More than often, this search is constraint to the existing equipment base, or a catalog search for standard equipment. It is then not surprising, that the product manufactured at large scale, either deviates significantly from the intended product, and/or the incurred costs to manufacture the product are much higher than anticipated because the equipment has not been designed for this product, or product range. This paper describes the combined design of an extruder equipment and the operating conditions to process ice cream with desired product attributes

Factory operations modelling methodology applied to a case study

Most of Unilever’s food processes consist of a large number of ingredients with limited storage capacity, a small number of process plants, a large number of intermediate product storage facilities and a smaller number of packing lines. The practical production scheduling inside the vast majority of these factories focusses on scheduling the packing lines on the production floor. The schedule is ‘thrown over the wall’ to the process department, in which a schedule is being made to satisfy the packing demand. This schedule is also "thrown over the wall" to the incoming materials department, etc. This way of scheduling poses three problems: 1. There is no clear insight in where the bottlenecks in the whole process are, resulting in a reduced production capacity. 2. Any change in the packing schedule might lead to an infeasible schedule in the upstream departments. As a result, packing lines may not run due to lack of intermediate products, wrong intermediates being made in the process plant, etc. 3. Each department will strive to ensure that their department is not to blame for not packing products, hence less available capacity will be communicated to the plant. The challenge is to reduce the impact of these problems in order to increase the capacity of the factory and reduce the product cost/tonnes. Problem This paper describes a methodology is to reduce the impact of these problems by modelling the factory operations. This is done by building a multi-stage scheduling model which describes the infra-structure of the factory, which products are being produced and how the plant is operated. The key challenge is to translate the complexity of the plant (and the operations) into a simplified, but realistic, multi-stage scheduling model. This model of the whole factory, including the constraints, is used to schedule the whole plant, maximising the production capacity and minimising the impact of the above described problems. Main results This paper describes a methodology to translate the complexity of the plant into a simplified model that can be used to schedule all relevant plant operations. The methodology consists of six steps: 1. Based the process flow diagrams, interviews and standard operating procedures a factory structure model is built. 2. Including the bill of materials and product routing a material flow structure is built. 3. Combining the above structures and taking into account the change-over structure the factory model is built. 4. This factory model is used to specify which data is to be retrieved from the existing factory systems into the data model. 5. The simulation model is implemented by combining the factory model, data model and the operational inputs. 6. The model is firstly verified with the operators in the plant, followed by validation by running the plant by the model. Conclusions & recommendations The above methodology was applied to an ice-cream factory. The resulting model was validated and shown to describe the plant with sufficient accuracy. Operational use of the model proved a production capacity increase of 10 – 30

Modelling and experimental validation of emulsification processes in continuous rotor-stator units

Despite the wide range of industrial applications of structured emulsions, current approaches toward process design and scale-up are commonly based on trial-and-error experimentation. As this design approach is foreseen to deliver most likely suboptimal process solutions, we propose in this contribution a model-based approach as the way forward to designing manufacturing processes of structured emulsions. In this context, process modelling and simulation techniques are applied to predict production rates and equipment sizing. Moreover, sensitivity analysis of the process model provides insight about potential bottlenecks in the process

Modelling and simulation of extensional-flow units in emulsion formation

Here we studied the emulsification process carried out in an extensional-flow unit. By means of rigorous population and momentum balances we captured the phenomenological description of the first principles occurring in such unit. The strong feature of our model approach resides in the fully mechanistic description of the governing phenomena. A population balance equation was formulated and solved to account for the disappearance and appearance of droplets at each size class. Coalescence mechanism was included to account for the instability of newly created droplets. We validated the accuracy of the results obtained from our equation-based model with experimental data obtained at pilot-plant scale. The results obtained by simulation showed that at a given set of operational conditions and pre-emulsion properties the product obtained was within the desired and narrow specifications space. As a concluding remark we suggest further exploring the design and development of extensional-flow units for structured emulsions

Aseptic processing of liquid foods containing solid particles

No abstract

Supercritical CO2 drying of foodstuffs in packed beds: Experimental validation of a mathematical model and sensitive analysis

In this contribution, a mathematical model is built to predict the changes in water concentration in both a solid food matrix and a fluid carrier during supercritical carbon dioxide (SC–CO2) drying. The mass balance equations of the model involve five dimensionless parameters: Peclet number modified Sherwood number, Fourier number, mass ratio and equilibrium constant. The differential equations were discretized using the finite explicit difference method. The resulting model was implemented and solved in Matlab/Simulink using an explicit Runge–Kutta solver. A very good agreement (ARD = 7.2%) between experimental data, obtained by an independent group, and the present model was observed. The axial dispersion diffusion coefficient seems not to play a significant role during the drying process. A sensitivity analysis revealed that the predictions are relatively more sensitive to the equilibrium constant and the mass ratio than to Peclet and modified Sherwood numbers. Furthermore, in the case of Peclet and modified Sherwood numbers, the sensitivity and the uncertainty of the output are function of the final moisture content. The present model could be used as an optimization tool for kinetic studies to investigate the effects of different operation conditions on the performance and design of the supercritical drying technology