286443 Making Decisive Decisions On Simulating Moving Bed Designs

Thursday, November 1, 2012: 4:05 PM
404 (Convention Center )
Paul Bussmann, Floor Boon and Ronald Vroon, Dutch Institute for Applied Science (TNO), Zeist, Netherlands

The working of an simulating moving bed (SMB) is still poorly understood. Although the SMB is used in food, petrochemical, and pharmaceutical industries, the operation is suboptimal and more widely applicable. Determining the design (CAPEX) and operating parameters (OPEX) of SMB processes remains a challenge. The optimization problem is complicated by the relative large number of decision variables (such as flow rates, particle size, column length and configuration) and various conflicting objectives (such as maximization of the adsorbent productivity and minimization of the water use for a required product recovery and purity).

A multi-objective optimization is adopted opposed to a single-objective optimization. Using a single-objective optimization, several objectives are combined into a single scalar objective function using arbitrary weight factors. Drawbacks are (i) the sensitivity to the values of the weighting factors used (which are difficult to assign on an a-priori basis) and (ii) the risk of losing some optimal solutions. The solution of a multi-objective optimization problem is given by an optimum Pareto set, which is a set of operating conditions such that no operating condition can be found that would lead to objective values which are all better that those in the Pareto set. 

The method consists of the following steps:

  1. Formulation of product and process requirements (in terms of e.g. recovery, purity and dilution);
  2. Adsorbent selection;
  3. Determination of the SMB configuration (required number of columns (or transfer units) per section, VARICOL, PowerFeed);
  4. Determining the theoretical working point using the triangle theory [Mazzotti et al., 1997];
  5. Sizing the SMB in the theoretical working point;
  6. Multi-objective optimization, resulting in a Pareto plot. An optimization tool within gProms® was developed, enabling single and multi-objective optimization of an SMB.

The approach was successfully developed and validated in the separation of an industrial hydrolysate obtained from agricultural side streams in high value carbohydrate fractions.


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