1.12. GOALS & OBJECTIVES

Isolation, screening and selection of microbes, capable of

producing amylase enzyme, from soil

Collection of microbes from local soil and NCIM, which are

responsible for producing α-amylase enzyme and ethanol

production

α - amylase production from isolated and collected cultures by

solid state (S.S.F) and submerged fermentations (SmF)

Optimization of α-amylase production by Response Surface

Methodology(R.S.M)

Development of neural network model and neuro fuzzy model

for enzyme production and comparison

Optimization of total protein and its activity by Genetic

Algorithm and Particle Swarm Optimization

Optimization of Enzyme mediated Saccharification of starch by

using microbial amylases

Conversion of sugars into Ethanol by employing Saccharomyces

cerevisiae and Zymomonas mobilis.

2.1. ETHANOL PRODUCTON

From 2007 to 2010, the share of ethanol in global gasoline type

fuel use increased in 2010, from 3.7% to 5.4%. In 2010, worldwide

ethanol fuel production reached 86.9 billion liters. Environmental

efficiency for agro-industry has been reviewed recently in relation to

analysis of the ecological, economical and social aspects (Borrero et.

al. 2003) and characteristics of anhydrous ethanol used as an

automotive fuel mixed with gasoline, have been described (Vilar et. al.

2003). The largest ethanol consuming region other than South

America (38%) is Asia, accounting for 20% of world consumption. In

fact, biomass plays an important role as a source of energy in Asia.

On the other hand, very little, if any, information is available on the

source of the biomass used as a source of energy.

The main exporters of ethanol are U.S.A., Brazil, France, South

Africa, and the United Kingdom. Sugarcane is produced in several

countries Africa (Zimbabwe, Kenya, Egypt, Zaire, Zambia, Sudan,

Swaziland, and Mauritius), and the ethanol production in this

continent accounts for the worlds production. The production of fuel

ethanol from biomass has been considered a laudable goal because

plant biomass is the only sustainable source of organic fuels,

chemicals, and materials available to humanity. Moreover, the

renewable energy sources are much more environmentally friendly

than fossil fuels. Following burning they are lost forever and pose no

problem of pollution comparable to fossil fuels. Thus, the extensive

use of renewable resources will slow down continued deterioration of

the environment (Wu et. al. I998; Wu et. al. 1999; Zafar et. al. 2005).

A model of ethanol fermentation considering the effect of

temperature was developed and validated. Experiments were

performed in a temperature range from 28 to 40oC in continuous

mode with total cell recycling using a tangential microfiltration

system. This work is reported by Atala, D., A. Costa, et al. (2001).

2.2. BIOLOGICAL ENERGY PRODUCTION

Lignocellulosic materials containing cellulose, hemicellulose, and

lignin are the most abundant renewable organic resource on earth.

The utilization of renewable resources for energy and chemicals is

expected to increase in the near future (Aristidou, A et.al., 2000).

Various plant biomass residues can also be converted to ethanol

rather than burning (Kadam et. al. 2000, Schugeri, 2000). These

include agricultural residues for example, bagasse from sugarcane,

corn fiber, rice straw and hulls, nutshells and farm silages (Driehuis

and Wikselaar, 2000) wood waste (sawdust, timber slash, and mill

scrap), the paper trash and urban yard clippings in municipal waste,

energy crops (fast growing trees like poplars, willows, and grasses like

switch grass or elephant grass) (Anderson et. al. 2005), the methane

captured from landfills, municipal waste water treatment, manure

from cattle or poultry and even from spent sulfite pulping liquor (SSL)

which is a high-organic content byproduct of acid bisulfite pulp

manufacture. Because of its moisture content sugar cane bagasse has

a low value fuel; therefore its conversion to ethanol has also been

considered (Teixeira et. al. 1999; Duchuis and van Wikselaar, 2000;

Gnansounon et. al.2005).

2.3. ETHANOL PRODUCTION BACTERIA VS. YEAST

According to P. Gunasekaran (1999), Zymomonas mobilis and

Saccharomyces cerevisiae and are better candidates for industrial

alcohol production. Zymomonous mobilis is better than Saccharomyces

cerevisiae with respect to ethanol productivity and tolerance.

Observed theoretical yield was 92%, if Zymomonous mobilis is

cultivated in batch fermentation process for 70hrs. But in a

continuous fermentation using mixed cultures of Zymomonous mobilis

and Saccharomyces cerevisiae, production of 54.3 g/L of ethanol was

observed with 98% theoretical yield within 3 days.

2.4. ETHANOL TOLERANCE

A fundamental problem limiting higher yields is the low ethanol

tolerance of yeasts. The adverse effects of ethanol on yeast growth,

viability, and metabolism are caused primarily by ethanol leakage of

the plasma membrane i.e., the increased membrane fluidity at higher

ethanol concentrations (Ingram, 1990; Lloyd et. al. 1993). Medium

composition, and many other surrounding environmental factors and

number of genes affect ethanol concentrations of yeasts (Chi et. al.

1991).

Traditionally, ethanol was produced in batch fermentation with

Saccharomyces cerevisiae strain that cannot tolerate high

concentrations of ethanol. Ethanol tolerance in yeast is a fundamental

problem in fermentation industry especially at higher temperatures. It

is not necessary that ethanol kills the cells, but rather temporarily

inactivate them in a reversible manner. Saccharomyces cerevisiae

cannot tolerate more than about 10-12% by volume of ethanol

(Taylor et. al. 1995; Taylor et. al. 1998) Ethanol tolerance in free and

immobilized Saccharomyces cerevisiae was measured , and the study

indicated that immobilized yeast was far less sensitive to the ethanol

(Desimone et. al. 2002).

Boyle, M., N. Barron, et al. (1997) have reported a thermotolerant,

ethanol-producing yeast strain Kluyveromyces marxianus IMB3 that

would grow at 45°C on media containing 2, 4 and 6 % (w/v) pulverized

barley straw supplemented with 2% (v/v) cellulase and produce

ethanol.

2.5. ETHANOL FERMENTATION BY IMMOBILIZATION OF CELLS

Converti, A., C. Sommariva, et al. (1994) have published results a

study carried out mathematical modelling of fermentation with

immobilized cells in a large-pore matrix.

Immobilization resulted in increased levels of ethanol tolerance

Ciesarová, Z., Z. Domeny, et al. (1998).

Immobilization has also been reported with Z. mobilis in fluidized

bed reactors with increased productivity(Davison, B. and C.

Scott.,1988).

Ethanol fermentation using immobilized cells of yeast is one the

widely has also been reported by others (Wendhausen et. al. 2001;

Shen et. al. 2003b). Most of the methods involve the use of chemicals

for pretreatment of carriers. Some of such chemicals proved toxic to

beverages; therefore, biodegradable cellulose sponge (loofa sponge) has

been opted as a safe and alternative carrier for cell immobilization

(Ogbonna et. al. 1994). Many matrices have been used for

immobilization such as flexible spongelike material (Scott and O’Reilly,

1995), agar gel, calcium alginate beads (Ogbonna et. al. 1989; Li,

1996; Sree et. al. 2000; Najafpour et. al. 2004), Kissiris (mineral glass

foam derived from lava) (Nigam et. al. 1997), carrageenan, in ethanol

fermentation.

2.6. RAW MATERIALS FOR ETHANOL PRODUCTION

Fermentation processes from any material that contains sucrose

can derive ethanol. Many and different raw materials used in the

production of ethanol through fermentation. They are starches, sugar

and cellulose materials. Sugars can be converted to ethanol directly.

Starches must first be hydrolyzed to fermentable sugars by the action

of enzymes. Once simple sugars are formed, enzymes from yeast can

readily ferment them to ethanol. Important promising raw materials

used for ethanol production are sugar, starch and cellulose materials.

Ethanol production has been reported from several substrates

(Table.2.1).

Table.2.1 Substrates for alcohol production

S.No Substrates Name of the mocrobe Proposed

method Author Year

1 sweet

potato

S.fibulyer &

Zymomonas mobilis SSF Sevendsby 1981

2 raw sweet

potato Rhizopus species

one step

process Matsuoka 1987

5 uncooked

starch Rhizopus strain SSF

Fang and

Wang 1989

6 raw starch Corticum rolfsii &

Schizosaccharomyces SSF Hariantono 1991

7 Beet

Molasses

Saccharomyces

cerevisiae fermentation

Yekta

G.KSUNGU

R

2001

8 ozonized

raw starch recombinant yeast SSF

Nakamura

& Sawada 2002

9

Molasses

Saccharomyces

cerevisiae fermentation

Ranulfo

Monte

Alegre

2003

10 soybean

molasses

Saccharomyces

cerevisiae fermentation

Paula F.

Siqueira 2008

2.7. AMYLASE PRODUCTION BY SOLID STATE FERMENTATION

Amylases used in breadmaking and to break down complex

sugars, such as starch, into simple sugars. Alpha and beta amylases

are important in brewing beer and liquor. Amylase production has

been reported from several substrates (Table.2.2 & Table.2.3)

Table 2.2. AMYLASE PRODUCTION

S.No Substrate Organism Author Year

1 Bacterial

bran B. licheniformis Ramesh 1989

2 Wheat bran B. coagulans Babu and

Satyanarayana 1995

3 banana fruit

stalk Bacillus sp. Krishna 1995

4 Wheat bran Bacillus sp Soni 2003

5 Tapioca/

rice husk B. subtilis Baysal 2003

6 Wheat bran Thermomyces

lanuginosus

Kunamneni,

A., K 2005

7 Wheat bran B. subtilis Singh, S. K 2008

8 Wheat bran Aspergillus niger Manikandan 2009

9 waste

Loquat Penicillium expansum Serkan Erdal 2010

Table 2.3. AMYLASE PRODUCTION BY SUBMERGED FERMENTATION

S.No Substrate Organism Author Year

1 wheat bran Aspergillus oryzae A. K. Kundu 1970

2 Cassava

pulp

Rhizomucor pusillus Tony M. Silva 2005

3 wheat bran Bacillus sp Sujata d 2010

4 rice bran Aspergillus sp.MK07 Murali

Krishna

Chimata

2010

5 various

starches,

flours

Bacillus subtilis Özdemir S 2011

6 wheat bran Bacillus

sp. KR-8104

Maryam

Hashem

2011

7 wheat bran Bacillus sp V. P. Zambare 2011

2.8. RESPONSE SURFACE METHODOLOGY (RSM)

Many scientists have worked on response surface optimization to

maximize enzymes production. Enzymes production processes have

been reported from several substrates and different microbes

(Table.2.4 & Table.2.5).

Table 2.4: RESPONSE SURFACE METHODOLOGY (RSM)

S.No Substrate Organism Author Year

1 Wheat

bran Aspergillus oryzae

Shah, N. K., V.

Ramamurthy 1991

2 wheat

bran Aspergillus niger M.P. NANDAKUMAR 1999

3 Wheat

bran

Bacillus

licheniformis Chen, Q.-h., H. Ruan 2007

4 wheat

bran

Bacillus

amyloliquefaciens Dhanya Gangadharan 2008

5 groundnut

oil cake

Bacillus

amyloliquefaciens

Dhanya Gangadharan

2008

6 agro-

residue Bacillus subtilis p.sudarshan 2009

7 agro-

residues B. subtilis KCC103 Gobinath Rajagopalan 2009

8 cassava Bacillus

brevis MTCC 752 Ramesh C 2009

9 starch and

glycerol Streptomyces4 Mihaela Cotârleţ 2011

10 Wheat

bran green microalgae Azma, M 2011

10 starch Saccharomyces

cerevisiae Yingling, B 2011

2.9. MODELING AND OPTIMIZATION

Koutinas, A. A., R. Wang, et al. (2004) have worked on

reestructuring and optimization of the conventional fermentation

industry for fuel and chemical production is necessary to replace

petrochemical production routes

ANN linked with genetic algorithm were used to find the optimum

concentration of L-asparaginase production by Aspergillus terreus

MTCC 1782 in submerged fermentation (Baskar, G. and S.

Renganathan (2010).

optimization strategy for design of experiments and back propagation

algorithm of. Components such as L-proline, sodium nitrate, L-

asparagine and glucose were identified as significant fermentation

media components using Plackett–Burman design

Gurunathan, B. and R. Sahadevan (2011) reported the sequential

optimization strategy for design of an experimental and artificial

neural network (ANN) linked genetic algorithm (GA) were applied to

evaluate and optimize media component for L-asparaginase

production by Aspergillus terreus MTCC 1782 in submerged

fermentation

Hashemi, M., S. M. Mousavi, et al. (2011) have done modeling for

different phases of the bacterial growth and the production of α-

amylase by Bacillus sp. in a solid-state fermentation process.

2.10. NEURAL NETWORK MODELING

A.J.G. da Cruz, C.O. Hokka and R.C. Giordano et al.(1997) worked

on the production of penicillin G by Penicillium chrysogenum and it

was simulated by employing a feed forward neural network.

Backpropagation and random search methods were used in this

method. It was observed that the nonlinear behavior of the process

was able to describe accurately.

Nikhil, Bestamin Özkaya, Ari Visa, Chiu-Yue Lin, Jaakko A.

Puhakka, and Olli Yli-Harja et al.(2008) have reported the

performance of a sucrose-based H2 production in a completely stirred

tank reactor (CSTR) was modeled by neural network back-propagation

(BP) algorithm. The H2 production was monitored over a period of 450

days at 35±1 ºC. The proposed model predicts H2 production rates

based on hydraulic retention time (HRT), recycle ratio, sucrose

concentration and degradation, biomass concentrations, pH,

alkalinity, oxidation-reduction potential (ORP), acids and alcohols

concentrations. Artificial neural networks (ANNs) have an ability to

capture non-linear information very efficiently. In this study, a

predictive controller was proposed for management and operation of

large scale H2-fermenting systems. The relevant control strategies can

be activated by this method. BP based ANNs modeling results was

very successful and an excellent match was obtained between the

measured and the predicted rates. The efficient H2 production and

system control can be provided by predictive control method combined

with the robust BP based ANN modeling tool.

Charef Chabbi, M. Taibi and B. Khier et al.(2008) described two

modeling techniwues.They ANN and the hybrid artificial neural

network. The ANN’s fail the justification, but the HANN is good in

interpolation even for fewer training data.

2.11. FUZZY AND NEURO FUZZY MODELING

Bioprocess parameters are highly nonlinear and it is difficult to

control. Good modeling and control of these variables facilitates real-

time identification of these regimes. A fuzzy neural network model for

a bioprocess was developed by Hamrita,T.K and ShuWang et al (2000)

based on decomposition of the process into its different regimes. The

method is illustrated through the development of a real-time product

estimation model for simulated gluconic acid batch fermentation.

Baruch, I.; Galvan-Guerra, R.; Mariaca-Gaspar, C.-R.; Castillo, O

et al(2008) have proposed a new recurrent fuzzy-neural multi-model

(FNMM) identifier applied for identification of a distributed parameter.

2.12. MULTI OBJECTIVE OPTIMIZATION

If more than one response is present, then multiobjective

optimization need to be used for optimal solution. Adrian Dietz,

Catherine Azzaro Pantel, Luc Guy Pibouleau, and Serge Domenech et

al(2007) worked on the case study on multiproduct batch plant for the

production of four recombinant proteins. A Genetic Algorithm with a

Discrete Event Simulator was used in this method. Genetic Algorithm

was developed with a Pareto optimal ranking method.

Sendin, J.O.H. Exler, O. Banga, J.R et al (2010) have reported

multi-criteria optimization in the field of systems biology. The authors

present a novel solution scheme based on a global optimization. The

authors shown how the set of optimal solutions successfully and

efficiently obtained, providing further insight into the systems under

consideration regarding their optimal solution.

Sandu TARA and Alexandru Woinaroschy et al (2011) have dealt

with economical and ecological multi-criteria optimization of the

bioprocess of pyruvic acid production from glucose using a mutant

Escherichia coli strain. The optimization is possible due to an

automatic and repeated exchange of variables between Matlab

optimization algorithms.

2.13. MULTI OBJECTVE OPTIMIZATION BT GENETIC ALGORITHM

M.V.R.K. Sarma, Vikram Sahai and V.S. Bisaria et al(2009) have

worked on Genetic Algorithm optimization. Genetic algorithm was

applied for the optimization of siderophore and biomass production in

shake flask experiments. This method also used successfully to

estimate the kinetic parameters of the mathematical models of the

batch fermentation

Zhang, h and zhang, z et al (2010) have done optimization of

penicillin using genetic algorithm. The proposed approaches can be

productively applied to optimization problems of fed-batch

fermentation to develop the operation of such processes.

2.14. MULTI OBJECTIVEOPTIMIZATION BY PSO

Carlos A. Coello Coello et al (2004) have presented an approach in

which Pareto optimal is incorporated into particle swarm optimization.

Other current proposals to extend PSO to solve multi objective

optimization problems,. Results specify that the approach is extremely

competitive and that can be measured a viable alternative to solve

multi objective optimization problems.

Nor Azlina Ab. Aziz et al (2011) worked on Particle swarm

optimization (PSO) is an optimization method that belongs to the

swarm intelligence family. It was initially introduced as continuous

problem optimization tool. It has evolved to being applied to more

complex multi objective and constrained problem. This paper presents

a systematic literature review of PSO for constrained and multi

objective optimization problems.

Leticia Cagnina, Susana Esquivel et al (2005) this paper proposes

a hybrid particle swarm optimization. The results are compared with

those obtained with the Pareto Archived Evolution Strategy and the

Multi-Objective Genetic Algorithm.

DESIGN OF THE PROJECT WORK

Fig: 3.1 Flow diagram for design of project work

Collection & Processing of the

Vegetable Wastes

Cutting& Grinding + Water----- Sterilization

α- amylase production modelling for protein prediction

α- amylase production optimization with GA & PSO

Enzyme mediated saccharification

optimization

Alcohol fermentation

Separation of alcohol by fractional

distillation

Ethyl Alcohol

Enzyme

Purification

Isolation& collection of microbes

from MTCC for

α- amylase production

Collection of microbes

from MTCC for ethanol

fermentation

1

Fig.3.2. Proposed flow diagram for α-amylase and ethanol production