Abstract: Batch production plants provide a wide range of
scheduling problems. In pharmaceutical industries a batch process
is usually described by a recipe, consisting of an ordering of tasks
to produce the desired product. In this research work we focused
on pharmaceutical production processes requiring the culture of
a microorganism population (i.e. bacteria, yeasts or antibiotics).
Several sources of uncertainty may influence the yield of the culture
processes, including (i) low performance and quality of the cultured
microorganism population or (ii) microbial contamination. For
these reasons, robustness is a valuable property for the considered
application context. In particular, a robust schedule will not collapse
immediately when a cell of microorganisms has to be thrown away
due to a microbial contamination. Indeed, a robust schedule should
change locally in small proportions and the overall performance
measure (i.e. makespan, lateness) should change a little if at all.
In this research work we formulated a constraint programming
optimization (COP) model for the robust planning of antibiotics
production. We developed a discrete-time model with a multi-criteria
objective, ordering the different criteria and performing a
lexicographic optimization. A feasible solution of the proposed
COP model is a schedule of a given set of tasks onto available
resources. The schedule has to satisfy tasks precedence constraints,
resource capacity constraints and time constraints. In particular
time constraints model tasks duedates and resource availability
time windows constraints. To improve the schedule robustness, we
modeled the concept of (a, b) super-solutions, where (a, b) are input
parameters of the COP model. An (a, b) super-solution is one in
which if a variables (i.e. the completion times of a culture tasks)
lose their values (i.e. cultures are contaminated), the solution can be
repaired by assigning these variables values with a new values (i.e.
the completion times of a backup culture tasks) and at most b other
variables (i.e. delaying the completion of at most b other tasks).
The efficiency and applicability of the proposed model is
demonstrated by solving instances taken from a real-life
pharmaceutical company. Computational results showed that
the determined super-solutions are near-optimal.
Abstract: Brown seaweeds are abundant in Portuguese coastline
and represent an almost unexploited marine economic resource. One
of the most common species, easily available for harvesting in the
northwest coast, is Saccorhiza polyschides grows in the lowest shore
and costal rocky reefs. It is almost exclusively used by local farmers
as natural fertilizer, but contains a substantial amount of valuable
compounds, particularly alginates, natural biopolymers of high
interest for many industrial applications.
Alginates are natural polysaccharides present in cell walls of
brown seaweed, highly biocompatible, with particular properties that
make them of high interest for the food, biotechnology, cosmetics
and pharmaceutical industries. Conventional extraction processes are
based on thermal treatment. They are lengthy and consume high
amounts of energy and solvents. In recent years, microwave-assisted
extraction (MAE) has shown enormous potential to overcome major
drawbacks that outcome from conventional plant material extraction
(thermal and/or solvent based) techniques, being also successfully
applied to the extraction of agar, fucoidans and alginates. In the
present study, acid pretreatment of brown seaweed Saccorhiza
polyschides for subsequent microwave-assisted extraction (MAE) of
alginate was optimized. Seaweeds were collected in Northwest
Portuguese coastal waters of the Atlantic Ocean between May and
August, 2014. Experimental design was used to assess the effect of
temperature and acid pretreatment time in alginate extraction.
Response surface methodology allowed the determination of the
optimum MAE conditions: 40 mL of HCl 0.1 M per g of dried
seaweed with constant stirring at 20ºC during 14h. Optimal acid
pretreatment conditions have enhanced significantly MAE of
alginates from Saccorhiza polyschides, thus contributing for the
development of a viable, more environmental friendly alternative to
conventional processes.
Abstract: Gluconic acid is one of interesting chemical products
in industries such as detergents, leather, photographic, textile, and
especially in food and pharmaceutical industries. Fermentation is an
advantageous process to produce gluconic acid. Mathematical
modeling is important in the design and operation of fermentation
process. In fact, kinetic data must be available for modeling. The
kinetic parameters of gluconic acid production by Aspergillus niger
in batch culture was studied in this research at initial substrate
concentration of 150, 200 and 250 g/l. The kinetic models used were
logistic equation for growth, Luedeking-Piret equation for gluconic
acid formation, and Luedeking-Piret-like equation for glucose
consumption. The Kinetic parameters in the model were obtained by
minimizing non linear least squares curve fitting.
Abstract: Nowadays, butyl acetate, a pineapple flavor has been applied widely in food, beverage, cosmetic and pharmaceutical industries. In this study, Butyl acetate, a flavor ester was successfully synthesized via green synthesis of enzymatic reaction route. Commercial immobilized lipase from Rhizomucor miehei (Lipozyme RMIM) was used as biocatalyst in the esterification reaction between acetic acid and butanol. Various reaction parameters such as reaction time (RT), temperature (T) and amount of enzyme (E) were chosen to optimize the reaction synthesis in solvent-free system. The optimum condition to produce butyl acetate was at reaction time (RT), 18 hours; temperature (T), 37°C and amount of enzyme, 25 % (w/w of total substrate). Analysis of yield showed that at optimum condition, >78 % of butyl acetate was produced. The product was confirmed as butyl acetate from FTIR analysis whereby the presence of an ester group was observed at wavenumber of 1742 cm-1.
Abstract: In this study, cometabolic biodegradation of
chloroform was experimented with mixed cultures in the presence of
various organic solvents like methanol, ethanol, isopropanol, acetone,
acetonitrile and toluene as these are predominant discharges in
pharmaceutical industries. Toluene and acetone showed higher
specific chloroform degradation rate when compared to other
compounds. Cometabolic degradation of chloroform was further
confirmed by observation of free chloride ions in the medium. An
extended Haldane model, incorporating the inhibition due to
chloroform and the competitive inhibition between primary
substrates, was developed to predict the biodegradation of primary
substrates, cometabolic degradation of chloroform and the biomass
growth. The proposed model is based on the use of biokinetic
parameters obtained from single substrate degradation studies. The
model was able to satisfactorily predict the experimental results of
ternary and quaternary mixtures. The proposed model can be used for
predicting the performance of bioreactors treating discharges from
pharmaceutical industries.