Batch Fermentation Networks Model for Optimal Synthesis, Design, and Operation

Abstract

This paper addresses an integral optimization of fermentation processes. The behavior of the fermentors is described by a set of algebraic and differential equations written as finite-difference equations in an equation-oriented environment. Unconventional constraints related to the number of batch items and connections among them, detailed kinetic models and operating costs corresponding to inoculum, and different available substrates are included in the model. The optimal number of units to be used in the process, their optimal operation policy (i.e., connected in series or in parallel working out of phase), as well as the optimal volume and operation of each unit, are determined simultaneously. The model is formulated as a sequence of nonlinear programming (NLP) problems.

A modeling strategy for integrated batch process development based on mixed-logic dynamic optimization

“…Previous works on optimization of processing unit sequences with dynamic profiles could avoid the ordering and combination of several multistage models associated to batch units either because the physical constraints of existing equipment units were not considered [26] or because batch process stages were represented by a single batch operation and the whole process could be represented by a unique multistage model -i.e. by associating one modeling stage with each task [60].…”

Multiperiod design and planning of multiproduct batch plants with mixed‐product campaigns

“…12 for each component x are embedded in the overall model as algebraic equations. A more detailed description of this modeling strategy is presented in the work by Corsano et al18…”

“…For units' performance, detailed models are adopted. A description of the development of these models is given by Corsano et al18 Mass balances are obtained from the following differential equations, which are written as algebraic equations using the trapezoidal method and included in the overall model. where C X , C S , C D , and C E are the biomass, substrate, nonactive biomass, and ethanol concentration (kg m −3 ), respectively, Y x/s is the biomass yield coefficient, Y x/p product yield coefficient, k s is the substrate saturation constant (kg m −3 ), μ is the specific growth rate of biomass (h −1 ), and μ max the maximum specific growth rate of biomass (h −1 ).…”

Processes Integration: Multiplant Complex vs Multipurpose Plant Assessment

“…For these stages, a superstructure that contemplates all possible configurations, or those chosen by the designer as feasible, is modeled and embedded in the global model. This superstructure model is formulated as an NLP, and the details can be obtained in ref 15. To simplify the formulation in this work, only units in series are considered as possible configurations.…”