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THE PREDICTION AND MONITORING OF THE PERFORMANCE OF NIMIX MIXERS - FT0123

Description
THE MOST COMMON MIXING PROBLEM IN THE FOOD INDUSTRY IS NON-HOMOGENEITY OF SUSPENSIONS. TOP-TO-BOTTOM HOMOGENEITY MUST BE ACHIEVED WITHOUT EXCESSIVE AGITATION THAT MIGHT PRODUCE SURFACE AERATION OR PARTICULATE DAMAGE. THIS NEED IS ADDRESSED DIRECTLY BY THE nimix JET-MIXING METHOD. HOWEVER, THE CONFLICTING NEEDS OF HOMOGENEITY WITHOUT AERATION OR PARTICULATE DAMAGE. THIS NEED IS ADDRESSED DIRECTLY BY THE NIMIX JET-MIXING METHOD. HOWEVER THE CONFLICTING NEEDS OF HOMOGENITY WITHOUT AERATION MEANS THAT THE PREDICTION OF THE INEVITABLY-NARROW OPERATING RANGE IS CRUCIAL; YET THERE IS NO PREDICTIBE MODEL FOR NIMIX. THE NEED FOR A MODEL IS HSARED BETWEEN PROCESSORS OF FOOD AND THISE IN THE PAINT, FOUNDRY POTTERIES AND OTHER SECTORS. THE CFD WORK WILL BIULD ON RECENT SUCCESS IN FINDING EXPERIMENTAL AGREEMENNT FOR CFD PREDICTION OF A JET MIXER, WHILE EXTENDING THE RHEOLLLLOGY RANGE COVERED. SURFACE FLOWS AND DRIVE FORCES WILL BE PREDICTED AND COMPARED WITH MEASUREMENTS. THIS TEST OF CFD IN RHEOLOGICAL RANGE COVERED . SURFACE FLOWS AND DRIVE FORCES WILL BE PREDICTED AND COMPARED WITH MEASUREMENTS. THIS TEST OF CFD IN RHEOLOGICAL COMPLEXITY WILL BE SUPPLEMENTED BY AN EXAMINATION OF THE SESITIVITY OF THE PREDICTIONS TO RHEOLOGY. THESE SENSITIVES, TOO WILL BE MEAUSRED EXPERIMENTALLY, WITH A VISION SYSTEM AND A CONVENTIONAL LOAD CELL IN THE DRIVE. SUCH MEASUREMENTS WILL REVEAL THE FEASIBILITY OF SURFACE FLOW VISCOMETRY AND AGITATION AUTOMATION.
Objective
TO DEVELOP A PREDICTIVE MODEL FOR A NIMIX JET MIXER IN RHEOLOGICALLY COMPLEX FLUIDS TO EXTEND THE RANGE FO RHEOLOGICAL COMPLEXITY FOR WHICH COMPUTATIONAL FLUID DYNAMICS IS ABLE TO MODEL FLOWS TO EXAMINE THE FEASIBILITY OF MONITORING RHEOLOGY AND OF AUTOMATING THE MIXING PROCESS BY MEASURING SURFACE FLOW PROFILES OR OTHER NON-CONTACTING MEANS.
Time-Scale and Cost
From: 1995

To: 1998

Cost: £726,871
Contractor / Funded Organisations
BHR Group Limited
Keywords
              
Fields of Study
Resource Efficient and Resilient Food Chain