Design and optimization of poly(hydroxyalkanoate)s production plants using alternative substrates

Abstract

In this work, we propose a Mixed Integer Nonlinear Programming (MINLP) model to determine the optimal design of a poly(hydroxyalkanoate)s (PHAs) production plant configuration. The superstructure based optimization model considers different carbon sources as raw material: glycerol (crude and purified), corn starch, cassava starch, sugarcane sucrose and sugarcane molasses. The PHA extraction section includes four alternatives: the use of enzyme, solvent, surfactant-NaOCl or surfactant-chelate. Model constraints include detailed capital cost for equipment, mass and energy balances, product specifications and operating bounds on process units. The resulting MINLP model maximizes the project net present value (NPV) as objective function and it is implemented in an equation oriented environment. Optimization results show the sugarcane-enzyme option as the most promising alternative (NPV = 75.01 million USD) for PHAs production with an energy consumption of 22.56 MJ/kg PHA and a production cost of 3.02 US$/kg PHA. Furthermore, an economic sensitivity analysis is performed.

Production of polyhydroxybutyrate and polyhydroxybutyrate-co-MCL copolymers from brewer’s spent grains by recombinant Escherichia coli LSBJ

“…However, 70% of the market for beer production in the USA is concentrated with just two manufacturers (AB InBev and MillerCoors), and manufacturing facilities thereof are also centralized, meaning that the supply chain optimization for upcycling of BSG is relatively simpler as compared to other lignocellulosic feedstocks. The yield of PHA per mass of substrate, the acquisition cost of the substrate, and logistical considerations are critical factors for determining the economic viability of upcycling an agro-industrial residue [6,65]. Assuming a 50% carbohydrate content and 100% conversion of sugar to PHA, i.e., disregarding that used for cell growth, etc., theoretical maximum yields of PHA from BSG are estimated to be ~ 0.2 g PHA per g BSG, more than double that achieved in this preliminary work.…”

“…However, current PHA production costs are significantly higher than petroleum-based plastics, which limits their commercial success [3]. In an economic analysis based on reported yields of 0.24 g/g from sugarcane molasses [5], Ramos et al estimated the hypothetical unit cost of production to be 3.02 USD/kg PHB [6], which is much more expensive than industry-standard petroleum-based plastics (polyethylene, polypropylene, etc.) priced at 0.82-2.12 USD/kg [7].…”

Economic feasibility of exploitation of a bioethanol by-product to obtain high-value biopolymers

“…Figure 1 shows the optimal topology for the PHAs production process based on Ramos et al [19] . The media upon which microorganisms are grown must be sterile in order to be free from all other forms of microbes.…”

“…Despite some economic studies evaluating several microorganisms, carbon sources, fermentation conditions, and extraction methods, simultaneous determination of an industrial process optimal flowsheet and its operating conditions is also required. In this sense, Ramos et al [19] developed a superstructure for the optimization of PHA synthesis process, using mathematical programming, , including mass and energy balances, design, and sizing equations, as well as detailed correlations for equipment units capital cost. Based on our previous work [9] , the aim of this work was to evaluate the economic feasibility of producing PHAs from vinasse using different microorganisms by employing the superstructure already developed [19] .…”

“…In this sense, Ramos et al [19] developed a superstructure for the optimization of PHA synthesis process, using mathematical programming, , including mass and energy balances, design, and sizing equations, as well as detailed correlations for equipment units capital cost. Based on our previous work [9] , the aim of this work was to evaluate the economic feasibility of producing PHAs from vinasse using different microorganisms by employing the superstructure already developed [19] . The objective function for the optimization problem is the maximization of the Net Present Value (NPV).…”