FIELD OF THE INVENTION
The present invention relates to a process for efficient bioremediation of fresh cheese whey wastewater (FCWW). More particularly, the present invention relates to a two step process for efficient bioremediation of FCWW using an algal species which involves coagulation of the FCWW and aerobic algal cultivation in the FCWW. The phycoremediation process not only helps in the organic waste reduction using algal species but also provides algal biomass and lipid and has application in biofuel production, biofertilizer etc.
BACKGROUND OF THE INVENTION
Wastewater treatment is the removal of impurities from industrial or sewage wastewater, before they reach aquifers or natural bodies of water such as rivers, lakes, estuaries, and oceans. In broad terms, water is said to be polluted when it contains enough impurities to make it unfit for a particular use such as drinking, swimming, or fishing. Water pollution, therefore, is caused primarily by the drainage of contaminated wastewater into surface water or groundwater, and wastewater treatment is a major element of water pollution control. Although water quality is affected by natural conditions but the water pollution usually implies pollution caused by human activity as the source of contamination. Wastewater is a complex blend of metals, nutrients, and specialized chemicals and recovery of these valuable materials can help to offset a community's growing demands for natural resources. Resource recovery concepts are evolving, and researchers are investigating and developing numerous technologies. Reclamation and reuse of treated water for irrigation, groundwater recharge, or recreational purposes are particular areas of focus.
The major sources of the wastewater includes domestic wastewater resulting from water use in residences, businesses, and restaurants, industrial wastewater comes from discharges by manufacturing, dairy and chemical industries. Also, rainwater in urban and agricultural areas picks up debris, grit, nutrients, and various chemicals, thus contaminating surface runoff water.

Dairy industry wastewater has fats, lactose, whey proteins, nutrients which play an important role to increase the biological oxygen demand (BOD) of water. With milk components dairy wastewater also contain detergents and sanitizing agents which are result of cleaning process increase the concentration of chemical oxygen demand (COD). There are many ways to reduce the BOD and COD, but biological treatment is the primary means and eco-friendly.
Dairy effluent mostly contains organic waste and hence, biological degradation is the most promising options for the removal of organic material. However, sludge formed, especially during the aerobic biodegradation processes, can lead to serious and costly disposal problems. This can be aggravated by the ability of sludge to adsorb specific organic compounds and even toxic heavy metals. The existing processes for the treatment of organic wastes have significant drawbacks particularly with respect to the removal of organic substances which are biodegrade slowly. The predominant method of biological treatment is through use of activated sludge, but biological treatment processes are relatively inefficient, produce a large volume of sludge that must be disposed of, and frequently do not meet current waste quality discharge requirements for the removal of non-biodegradable or slowly biodegradable organic substances. However, biological treatment has the benefits of microbial transformations of complex organics and possible adsorption of heavy metals by suitable microbes. Biological treatments of waste treatment have great potential for combining various types of biological schemes for selective constituent's removal.
JP10165991 discloses a first processing step consisting of a wastewater containing biological flame degradable organic substances, and ozone treatment process and biological treatment step of treating with ozone in processing, and then is obtained by the processing method of the biological decomposition-resistant organic substance-containing wastewater, characterized in that the treatment with the second treatment step consisting of a reforming step and biological treatment step of treating the active species. However, the cost of the chemical oxidation is high, and there is a problem that the apparatus is complicated.

JP10118696 discloses a method of treating Solids Retention Time (SRT) with membrane bioreactor (MBR) for a long time and keeping biological treatment, in order to keep SRT for a long time, a large amount of biological metabolites are produced and membrane clogging is severe, and a large concentration of membrane area is required for low concentration drainage and results in a high cost method.
Therefore, there is a need of providing a process for efficient bioremediation of FCWW using algal species which not only helps in the organic waste reduction but also provides algal biomass and lipid and has application in biofuel production, biofertilizer etc.
OBJECT OF THE INVENTION
The main object of the present invention is to provide a two-step process for efficient bioremediation of FCWW using an algal species which involves coagulation of the FCWW and aerobic algal cultivation in the FCWW.
Another object of the present invention is to provide a process for efficient phycoremediation of FCWW that helps in the organic waste reduction using algal species and also provides algal biomass and lipid.
Yet another object of the present invention is to provide a process for efficient phycoremediation and has application in biofuel production, biofertilizer etc.
SUMMARY OF THE INVENTION
In an embodiment, the present invention provides a two-step process for efficient phycoremediation of cheese wastewater which involves coagulation of the FCWW followed by aerobic algal cultivation in the FCWW. The bioremediation process not only helps in the organic waste reduction using algal species but also provides algal biomass and lipid and has application in biofuel production, biofertilizer etc.

In another embodiment, the present invention provides a process for efficient phycoremediation of FCWW comprising the steps of: (a) dissolving a natural biodegradable coagulant in a desired concentration of acetic acid at desired temperature for desired time with stirring to obtain a solution; (b) mixing the solution obtained in step (a) in a FCWW and pre-treating the FCWW under optimal conditions for coagulation process and determining removal efficiency of different responses by performing jar test and allowing the solution to settle for an hour to obtain a supernatant; (c) cultivating separately C. Pyrenoidosa NCIM 2738 in a desired amount of the supernatant obtained in step (b) to be used as a medium; (d) inoculating sterile supernatant with desired amount of the inoculum obtained in step (c); and (e) incubating the inoculated supernatant obtained in step (d) at optimal conditions followed by intermittent shaking for 3-4 times a day and monitoring algal growth at 680 nm.
BRIEF DESCRIPTION OF THE DRAWING
The object of the invention may be understood in more details and more particularly description of the invention briefly summarized above by reference to certain embodiments thereof which are illustrated in the appended drawings, which drawings form a part of this specification. It is to be noted, however, that the appended drawings illustrate preferred embodiments of the invention and are therefore not to be considered limiting of its scope, for the invention may admit to other equally effective equivalent embodiments.
Figure 1 describes individual maximum response of total solids (TS) removal, turbidity (TUR) removal and COD removal respectively in accordance with the present invention.
Figure 2 describes multi-objective (MO) optimization condition to maximize removal efficiency of all individual response i.e. total solids (TS) removal, turbidity (TUR) removal and chemical oxygen demand (COD) removal.

Figure 3 describes growth of Chlorella pyrenoidosa NCIM 2738 measured by absorbance method in accordance with the present invention.
Figure 4(a) is a graphical representation of percent distribution of costs and income per year with respect of distribution of capital cost in accordance with the present invention.
Figure 4(b) is a graphical representation of percent distribution of costs and income per year with respect of distribution of operating cost in accordance with the present invention.
DETAILED DESCRIPTION OF THE INVENTION
The present invention will now be described in detail hereinafter with reference to the accompanying drawings in which a preferred embodiment of the invention is shown. This invention may, however, be embodied in many different forms and should not be construed as being limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure will be thorough, and will fully convey the scope of the invention to those skilled in the art.
In the main embodiment, the present invention provides a two step process for efficient phycoremediation of FCWW which involves coagulation of the FCWW and aerobic algal cultivation in the pretreated FCWW. The bioremediation process not only helps in the organic waste reduction using algal species but also provides high algal biomass yield and lipid productivity and has application in biofuel production, biofertilizer etc.
In another embodiment, the present invention provides a process for efficient phycoremediation of FCWW comprising the steps of: (a) dissolving a natural biodegradable coagulant in desired concentration of acetic acid at desired temperature for desired time with stirring to obtain a solution; (b) mixing the solution obtained in step (a) in a FCWW and pre-treating the FCWW under optimal conditions for coagulation process and determining removal efficiency of different responses by performing jar test and allowing the solution to settle for an

hour to obtain a supernatant; (c) cultivating separately C. Pyrenoidosa NCIM 2738 in the desired amount of the supernatant obtained in step (b) to be used as a medium; (d) inoculating sterile supernatant with desired amount of the inoculum obtained in step (c); and (e) incubating the inoculated supernatant obtained in step (d) at optimal conditions followed by intermittent shaking for 3-4 times a day and monitoring algal growth at 680 nm. The natural biodegradable coagulant includes but is not limited to chitosan. The desired concentration of the acetic acid is 0.1M and the desired temperature is 60°C for 5 hours.
In yet another embodiment, the present invention provides a process for efficient phycoremediation of FCWW with the responses which includes chemical oxygen demand (COD), total solids (TS) and turbidity (TUR) and multi-objective (MO). The process has biomass productivity in the range of 2.65 gL"1 - 2.75 gL"1 when the supernatant is TS pre-treated FCWW; the process has lipid productivity in the range of 75 mgL^d"1 - 79 mgL^d"1 when the supernatant is MO pre-treated FCWW. The optimal conditions of different responses include pH in the range of 5.0-9.0, coagulant dose in the range of 40-200 mgL1, stirring speed in the range of 30-150 rpm and stirring time in the range of 10-70 min. The process has biomass and lipid productivity increase by 3.19-fold and 4.77-fold respectively in C. pyrenoidosa grown in multi-objective MO pre-treated FCWW as compared to C. pyrenoidosa grown in BG11 medium as a control.
EXAMPLE 1 Experimental design for coagulation parameters and optimization
The four important operational process parameters for coagulation of FCWW are taken into consideration namely initial pH, chitosan dose, stirring speed and mixing time. Response surface methodology (RSM) was applied to develop the design model of selected operational process parameters. The COD, TUR and TS removal efficiency were considered as responses. To predict the optimal point, a second order quadratic model was fitted to the experimental results, as presented in the equation (1):

Y = /?0 + Z?=1 pt Xt + Z?=1 ft, Jtf + Z?=1 Z7=1 Ay XiXj (1)
where Fis the predicted response, n is the number of factor variables, X is the coded levels of the independent variables, p0 is the offset term, /?,- is the ith linear coefficient, /?«■«■ is the if1 quadratic coefficient, and /% is the if interaction coefficient. The statistical software package of MINITAB® 17.0 was used to design experiments and to obtain response surface model. The significance of the model was evaluated using analysis of variance (ANOVA).
EXAMPLE 2 Multi-objective (MO) optimization analysis
The desirability function approach was used to obtain the maximum COD, TUR and TS removal simultaneously. Derringer's approach of one-sided transformation was applied and the desirability function would be obtained as mentioned below in equation (2):
rmin
ifY Ymax
where, Ymm is the minimum acceptable value of Y, Y"" is the maximum value that is considered desirable and r is a constant with positive value.
EXAMPLE 3 Optimized conditions for pre-treatment of the high strength dairy wastewater
Optimized level of process parameters was observed viz. initial pH of 6.49,chitosan doses of 78.79 mgL1, stirring speed of 150 RPM and stirring time of 70 min to maximize the COD removal. The projected optimum level of initial pH, chitosan dose, stirring speed and stirring time maximize the TUR removal was 8.79, 151.51 mgL1, 65 RPM and 50.61 min. respectively. Whereas, for the

maximum TS removal the optimum level of parameters were 7.3 initial pH, 67.47 mgL1 chitosan dose, 114 RPM stirring speed and 45.15 min stirring speed. The predicted maximum individual responses were observed to be 72.12%, 50.92%, 74.80% for percentage of COD, TUR and TS respectively as shown in Figure 1. The maximum predicted response values of COD, TUR and TS removal efficiency for MO optimization were 68.10%, 73.63% and 47.80% respectively at the optimum level of initial pH (7.38), chitosan doses (106.26), stirring speed (103 RPM) and stirring time (42.12 min) as shown in Figure 2.
Figure 3 further describes growth of Chlorella pyrenoidosa NCIM 2738 which is measured by absorbance method. Different pre-treated FCWW was obtained from process optimized conditions after CCD experiment for different responses i.e. individual responses are chemical oxygen demand (COD) removal, total solids (TS) removal, turbidity (TUR) removal and Multi-objective (MO) - all three individual responses together) for cultivation of C. pyrenoidosa NCIM 2738.
EXAMPLE 4 Pre-treatment of Fresh Cheese Whey Wastewater
Table 1 represents the experimental design matrix with range of selected operational parameters and the experimental value of the responses. Among the thirty-one experiments, the level of variation in COD, TUR and TS were observed 55.8-68.7%, 23.4-48.7% and 54.4-73.6%. The multiple regression analysis was performed on the CCD-RSM experimental data to find out relationship between operational parameters (initial pH, chitosan dose, stirring speed and stirring time) with the responses (COD, Turbidity, and TS). The application of CCD-RSM approach yielded the following second-order polynomial equations for the respective responses. The equation for the removal efficiency of turbidity, COD and TS in terms of coded parameters value is given below in equation (3)-(5):

Table 1
Central composite design (CCD) in real and coded values with various
experimental responses for chemical oxygen demand (COD), turbidity (TUR)
and total solids (TS) reduction

Run orde r Variables and their levels Observed values

PH Chitosan dose (mg ml/1) Stirring
speed
(RPM) Stirring
time
(Min.) % of
COD
removal % of
turbidity
removal % of TS removal
1 6(-l) 80 (-1) 60 (-1) 25 (-1) 61.59±1.5 9 25.99±1. 18 59.82±1.37
2 8(+l) 80 (-1) 60 (-1) 25 (-1) 64.88±2.0 0 35.89±0. 92 58.99±1.57
3 6(-l) 160 (+1) 60 (-1) 25 (-1) 63.52±1.6 8 28.75±0.
7 63.69±1.67
4 8(+l) 160 (+1) 60 (-1) 25 (-1) 66.87±2.3 4 44.17±1.
25 59.79±0.23
5 6(-l) 80 (-1) 120(+1) 25 (-1) 57.42±1.3 4 38.80±1. 51 64.70±0.80
6 8(+l) 80 (-1) 120(+1) 25 (-1) 62.13±1.1 1 44.82±0. 85 70.01±1.08
7 6(-l) 160 (+1) 120(+1) 25 (-1) 61.06±1.8 0 35.85±0. 81 58.95±1.58
8 8(+l) 160 (+1) 120(+1) 25 (-1) 63.77±1.6 1 44.89±1.
57 62.18±0.86
9 6(-l) 80 (-1) 60 (-1) 55 (+1) 61.39±1.6
2 28.32±0.
35 63.46±0.79
10 8(+l) 80 (-1) 60 (-1) 55 (+1) 60.56±0.4 4 43.38±1. 24 59.13±0.18
11 6(-l) 160 (+1) 60 (-1) 55 (+1) 57.22±1.6
7 28.08±0. 84 63.40±0.32
12 8(+l) 160 (+1) 60 (-1) 55 (+1) 55.79±1.0 9 48.41±1. 00 56.99±0.74
13 6(-l) 80 (-1) 120(+1) 55 (+1) 68.10±2.2 8 34.83±0. 98 69.29±1.17
14 8(+l) 80 (-1) 120(+1) 55 (+1) 65.67±1.9 9 43.26±0. 49 71.59±0.91
15 6(-l) 160 (+1) 120(+1) 55 (+1) 62.69±1.2 3 27.89±1. 12 60.60±1.62
16 8(+l) 160 (+1) 120(+1) 55 (+1) 61.20±1.5
7 42.34±1. 46 60.83±0.74
17 5 (-2) 120 (0) 90(0) 40(0) 58.92±1.4 8 23.36±0. 62 62.99±0.11
18 9 (+2) 120 (0) 90(0) 40(0) 62.57±1.7 0 48.71±0. 70 61.89±1.45
19 7(0) 40 (-2) 90(0) 40(0) 61.69±1.1 6 36.51±1. 08 70.37±1.93
20 7(0) 200 (+2) 90(0) 40(0) 59.00±1.4 6 38.09±0. 81 62.48±0.59
21 7(0) 120 (0) 30 (-1) 40(0) 61.68±1.1
5 35.11±1. 00 54.36±0.92

22 7(0) 120 (0) 150 (+2) 40(0) 64.98±0.6 3 41.10±1. 28 62.58±0.42
23 7(0) 120 (0) 90(0) 10 (-2) 64.11±0.9 8 39.78±1. 42 62.63±0.59
24 7(0) 120 (0) 90(0) 70 (+2) 65.20±1.3 4 37.30±0. 99 64.42±0.76
25 7(0) 120 (0) 90(0) 40(0) 68.67±1.7 0 45.95±0. 98 73.05±1.68
26 7(0) 120 (0) 90(0) 40(0) 66.81±0.9 3 46.20±0. 87 73.05±0.90
27 7(0) 120 (0) 90(0) 40(0) 67.87±0.5 8 45.95±1.
25 73.55±1.74
28 7(0) 120 (0) 90(0) 40(0) 67.67±0.2 9 45.95±0. 80 71.55±0.45
29 7(0) 120 (0) 90(0) 40(0) 68.67±1.7 0 45.87±1. 46 73.05±1.84
30 7(0) 120 (0) 90(0) 40(0) 68.24±1.1 0 45.70±1. 45 72.05±0.31
31 7(0) 120 (0) 90(0) 40(0) 67.81±0.4 9 46.70±0.
25 72.55±0.72
a The observed values of chemical oxygen demand (COD), turbidity (TUR), and total solids removal were the mean values of triplicates with standard deviation (mean±SD).
COD removal (%) = 67.959 + 0.6324 - 0.6255 + 0.700C - 0.268D -
1. .829 44 - 1.92955 - .1.183CC - 0.8S2DD - 0.10145 - 0.0SSAC -
1.2644D + 0.0265C - 1.7505D + 2.199CD (3)
TUR removal (%) = 46.048 + 6.2234 + 0.3435 + 1.736C - 0.318D -2.54844 - 2.23355 - 2.030CC - 1.921DD + 1.23945 - 1.4224C + 1.117AD -1.6625C - 0.702BD - 1.838CD
(4)
TS removal (%) = 72.693-0.275 - 1.9315 + 2.055C + 0.448D - 2.56444 -
1.56855 - 3.556CC - 2.293DD - 0.58145 + 1.659AC - 0.7514D - 2.2185C -
0.796BD + 0363CD (5)
where, A is initial pH, B is chitosan dose (mgL1), Cis stirring speed (RPM) and D is stirring time (min).
To evaluate the feasibility of high strength FCWW based algal oil process, a process was developed in order to produce 10 barrels (1590 L) of algal oil per day. The key input assumptions with respect to FCWW based algal oil process are summarized in Table 1. The pre-treated wastewater from primary clarifier after

coagulation was sent to ORP system via gravity. Construction, operation and labour impacts were included within the system boundaries. The facility operated 365 days a year and achieve with adequate solar radiation and average algal cell productivity (0.244 kg m3 d"1) for the raceway pond based on laboratory study. A total of 50 units of ORP, each having 500 m long, 20 m wide and 0.5 m in-depth is required and operating depth of raceway ponds are assumed 0.4 m. The total area required for ORP and other facility of algal plant are 55 hectares for continuous mode of operation. The algal biomass was harvested by lamellae separator followed by centrifugation and concentrated up to 20% algae solid slurry and the extraction of oil is performed by algal slurry obtained after centrifugation using hexane. The algal slurry was sun drying for oil extraction. Solvent requirement for oil extraction from algae dry mass is 10:1 with 1 h recycle time and contributing an addition cost due to 10% solvent loss per day. Three main economical values products (chitosan FCWW sludge (CFS), algal oil and lipid extracted algal biomass (LEA)) are produced from the developed process, The CFS, algal oil and LEA is useful as biofertilizer, biodiesel and nutrients supplement, respectively.
To check the adequacy of the model the analysis of variance (ANOVA) was performed. According to the ANOVA analysis, the developed quadratic regression models for COD, turbidity and TS removal were highly significant as indicated by the high lvalue 31.56, 243.85 and 144.79 for COD, TUR and TS removal (%), respectively as shown in Table 2.
The goodness of fit for all the three individual models were evaluated by adjusted determination coefficient {R\&]) value. The results showed high R2Ai] values 87.52%, 98.24 and 97.06 for equation 3-5, respectively indicating a reasonable agreement between observed and predicted values for all three responses and suggests that the model equation in present invention provides satisfactory and accurate results. In addition, the high value of multiple correlation coefficient and adequate precision for COD removal (0.9034 and 22.92), turbidity removal (0.9864 and 66.19) and TS removal (0.9773 and 59.61) also indicate the suitability of models in terms of independent variables as shown in Table 2. Assessment of

single and multi-response optimization results of FCWW coagulation is summarized in Table 2. For the maximization of single response by optimized variable were observed experimentally 70.64% for COD removal (39.70% TUR removal; 63.26% TS removal), 72.52% for TS removal (67.71% COD removal; 41.28% TUR removal) and 48.21% for TUR removal (62.56% COD removal; 65.58% TS removal). The MO response optimization to enhance removal efficiency of TUR, COD, and TS was experimentally 42.77%, 64.88%, and 67.10% removal.
Table 2
Model coefficient estimate by multiple regression analysis for percentage of
chemical oxygen demand (COD), percentage of turbidity removal and
percentage of total solids removal

Term COD removal (%) Turbidity removal (%) Total solid removal (%)

Coeff SE t value P
valu
e Coeff SE t value P
valu
e Coeff SE t value P
valu
e
Consta nt 67.95 9 0.35 6 190.8
2 0.00 0 46.04 8 0.26 0 177.3 9 0.00 0 72.69 3 0.25 9 280.6 6 0.00 0
A 0.632 0.19
2 3.29 0.00
2 6.223 0.14 0 44.39 0.00 0 0.275 0.14 0 -1.97 0.05
5
B 0.625 0.19
2 -3.25 0.00
2 0.343 0.14 0 2.45 0.01 8 1.931 0.14 0 13.81 0.00 0
C 0.700 0.19
2 3.64 0.00
1 1.736 0.14 0 12.38 0.00 0 2.055 0.14 0 14.69 0.00 0
D 0.268 0.19
2 -1.39 0.17 0 0.318 0.14 0 -2.27 0.02 8 0.448 0.14 0 3.20 0.00
2
A.A 1.829 0.17 6 10.38 0.00 0 2.548 0.12 8 19.83 0.00 0 2.564 0.12 8 20.01 0.00 0
B.B 1.929 0.17 6 10.95 0.00 0 2.233 0.12 8 17.38 0.00 0 1.568 0.12 8 12.24 0.00 0
C.C 1.183 0.17 6 -6.72 0.00 0 2.030 0.12 8 15.81 0.00 0 3.556 0.12 8 27.75 0.00 0
D.D 0.852 0.17 6 -4.84 0.00 0 1.921 0.12 8 14.96 0.00 0 2.293 0.12 8 17.90 0.00 0
A.B 0.101 0.23 6 -0.43 0.67 1 1.239 0.17
2 7.21 0.00 0 0.581 0.17 1 -3.39 0.00
1
A.C 0.055 0.23 6 -0.24 0.81
5 1.422 0.17
2 -8.28 0.00 0 1.659 0.17 1 9.69 0.00 0
A.D 1.264 0.23 6 -5.37 0.00 0 1.117 0.17
2 6.51 0.00 0 0.751 0.17 1 -4.39 0.00 0
B.C 0.026 0.23 6 0.11 0.91
2 1.662 0172 -9.68 0.00 0 2.218 0.17 1 12.95 0.00 0
B.D 1.750 0.23 6 -7.43 0.00 0 0.702 0.17
2 -4.09 0.00 0 0.796 0.17 1 -4.65 0.00 0
CD 2.199 0.23 6 9.34 0.00 0 1.838 0.17
2 10.71 0.00 0 0.363 0.17 1 2.12 0.03 9

A - Initial pH, B - Chitosan dose, C - Stirring speed and D - Stirring time; Coeff. -coefficient; SE- standard error; Goodness of fit: % COD removal model: R2=9036; % of turbidity removal: R2= 98.64%; % of total solids removal: R2= 97.73%.
Further pre-treated FCWW obtained from all individually and multi-response optimized conditions were used as growth medium for C. pyrenoidosa NCIM 2738. From growth analysis, MO optimized FCWW nutrients are better for algal growth than other individual optimized FCWW nutrients. It was observed that maximum biomass of 2.70 gL1 was obtained in FCWW pre-treated for TS removal followed by 2.44 gl/^JO gL4and 2.26 gL4for FCWW pre-treated for MO, maximum TUR and COD removal responses respectively. Similarly, the maximum lipid productivity of 77.41 mgl/d"1 was obtained for FCWW pre-treated MO response followed by 75.02 mgl/d"1, 65.55 mgl/d"1 and 58.84 mgL" 1d~1 for FCWW pre-treated for maximum TS, COD and TUR removal responses respectively as shown in Table 3. The improvement in biomass and lipid productivity of C. pyrenoidosa grown in MO pretreated FCWW showed 3.19- and 4.77-fold increase in comparison with C. pyrenoidosa grown in BG11 medium. The maximum lipid accumulation (31.62% (w/w) was observed in MO treated FCWW which is 1.5-fold higher than that of control (21.05% w/w) as shown in Table 3. C. pyrenoidosa effectively grown in pre-treated FCWW (COD -15.45 g/L), this attribute makes them an efficient and eco-friendly tool to remediate wastewater at low cost.

Table 3
Comparison of biomass and lipid concentration and productivity of Chlorella
pyrenoidosa NCEVI 2738 after 10 days cultivation in pre-treated (individual and
multi-objective) process optimized FCWW

Mediu
m
conditio
ns Initi
al
pH Chitos
an
dose
(mgL Stirri
ng
speed
(RP
M) Stirri
ng
time
(min.
) Biomass Lipid Improvem
entin
productivit
y(fold)

Cell
Concentrat
ion (gL1) Biomass Productiv
ity
(mgL-1*1) Lipid content
(% w/w) Lipid productiv Bioma Lipi ity (mgL" ss d
Control (BGll) - - - - 0.766±0.05 76.6±5.0 21.05±1. 97 16.22±2. 56 - -
Individual response optimization
COD 6.49 78.78 150 70.00 2.26±0.09 226±9.0 28.95±1. 33 65.55±5. 61 2.95 4.04
TS 7.30 67.47 113 45.15 2.70±0.08 270±8.0 27.74±1. 50 75.02±6.
27 3.52 4.63
TUR 8.79 151.51 65 50.61 2.30±0.01 230±1.0 25.58±1. 21 58.8412. 79 3.00 3.63
MO 7.38 106.26 102 42.12 2.44±0.07 244±7.0 31.62±1. 43 77.4115. 56 3.19 4.77
COD-Chemical oxygen demand removal; TS-total solids removal; TUR-turbidity removal and MO- multi-objective optimization.
Table 4 shows the nutrient consumption profile of C. pyrenoidosa in different pre-treated FCWW. The COD removal was more than 61% in all the process optimized medium, the maximum COD removal rate of 1.05 gL"1^1 was observed in process optimized for COD by the 8th day followed by 1.02 gL^d"1, 0.975 gL4d" *and 0.88 gl^d"1 for turbidity (TUR), multi-response (MO) and total solids (TS) respectively by 10 days after this the declination of algal growth was observed. Reduction in COD suggests that C. pyrenoidosa, able to grow auto-heterotrophically and utilized organic compounds present in FCWW as a carbon source and as a source of energy. In the present invention, the high strength COD in range of 13-17 gL"1 wastewater is used directly for algal cultivation. The nitrate

reduction was more than 90% in all the CCD processed condition, the final nitrate concentration reached up to 0.58 mgL"1 (99.05 %) i.e. maximum among all CCD processed medium, followed by 96.88%, 92.16% and 90.23% for FCWW processed for COD, TS and MO respectively by 10 days. The consumption of phosphate i.e. responsible for the energy transfer cell membrane formation and nucleic acid was found faster in first 2 days after that it became slower. The phosphorus reduction profile as shown in Table 4 in all the conditions the phosphorus reduction was >74% in the CCD processed conditions the maximum reduction was found to be 81.82% for medium processed for TUR removal. The nutrient reduction by microalgae C. pyrenoidosa after 10 batch cultivation in FCWW, shows that this strain is suitable for FCWW remediation.
Table 4
Post-treatment of FCWW by Chlorella pyrenoidosa NCEM 2738 grown in
different process optimized pre-treated FCWW and parameters profile

Parameters Concentration (removal %)
DayO Day 2 Day 4 Day 6 Day 8 Day 10
CCD processed fresh cheese whey for maximum COD removal
COD 12.93 11.24 8.85 7.33 4.53 4.98
(gL4) (13.04) (31.55) (43.31) (64.96) (61.48)
Nitrate 51.29 32.35 15.74 12.94 10.53 1.6
(mgL1) (36.93 ) (69.31) (74.77) (79.47) (96.88 )
Phosphate 199.13 103.19 55.47 54.64 51.27 47.51
(mgL1) (48.18) (72.14) (72.56) (74.25 ) (76.14)
CCD processed fresh cheese whey for total solids TS removal
COD (gL1) 14.21 13.33 12.67 10.94 6.25 5.33
(6.19) (10.84) (23.01) (56.02) (62.49 )
Nitrate (mgL1) 57.88 31.97 24.22 16.23 9.16 4.54
(44.77) (58.15) (71.95) (84.17) (92.16)
Phosphate 177.61 91.09 83.78 51.78 45.85 44.48
(mgL1) (48.71) (52.83 ) (70.85 ) (74.19) (74.96 )
CCD processed fresh cheese whey for maximum TUR removal
COD (gL"1) 16.47 13.93 10.82 7.85 6.67 6.25
(15.42) (34.30) (52.3 ) (59.50) (62.05 )
Nitrate (mgL1) 61.17 37.42 17.73 11.39 5.02 0.58
(38.83 ) (71.01) (81.38) (91.79) (99.05 )
Phosphate 205.80 89.88 69.22 54.43 45.48 45.46
(mgL1) (56.33 ) (66.7) (73.55 ) (77.90) (77.91 )
CCD processed fresh cheese whey for multi -response optimization
COD (gL1) 15.45 14.67 12.67 10.84 9.8 5.7
(5.05 ) (17.99) (29.84) (36.57) (63.11)
Nitrate (mgL1) 57.0 30.13 12.17 10.06 10.58 5.57

(47.14) (78.65) (82.35) (81.44) (90.23) Phosphate 182.87 90.41 87.29 58.36 50.27 46.97
(mgL1) (50.56) (52.27) (68.09) (72.51) (74.32)
Initial average COD, nitrate and phosphate concentration were observed to be 44.00 gL'1- 133.34 mgL'
1 and 295.27 mgL'1 in FCWW, respectively
The capital cost involved in the development of basic infrastructure need to FCWW coagulation and algae cultivation are estimated as per assumptions of Guo et al. (2014), Rogers et al. (2014) and Hoffman et al. (2017), that account around $ 0.884 million as shown in Table 5A. The payback period was decided to be 10 years which accounts approximately $0.09 million year1. The highest capital investment for the system was land (~$0.041 million year1) accounts 46.29 % of the total project investment. The capital investment for the initial hexane purchase was ~$0.014 million year1 (15.71%) followed by coagulation system was ~$0.013 million year1 (14.18%), piping (~$0.012 million year1, 13.26%), ORP earthwork (~$0.005 million year1, 5.70%), and site development (~$0.004 million year1, 4.56%) as shown in Figure 4(a). The operation cost for various items involved in the process was adopted from technical economic studies (Rogers et al., 2014; Davis et al., 2011; Xin et al., 2016; Hoffman et al, 2017) and shown in the Table 5B. An OpEx account was calculated $ 9.79 million year1. The OpEx of FCWW based algal biofuel plant for coagulant (chitosan) were the highest among the operating cost (79.78%) followed by maintenance/insurance/tax (12.05%), hexane consumption (5.14%), labour (1.27%), onsite pumping (0.77%) and lipid extraction (0.05%) as shown in Figure 4(b). The combine electricity and hexane for algal oil extraction was $0,002 million year4 and $0.0005 million year \ respectively. The total biomass produced by algae cultivation in pre-treated FCWW was 4.63 tonne"1 and produced 3653.3 barrels annually. The volumetric production of dried sludge waste from FCWW after coagulation process is considered to be 33.6 kg m3 d"1 as shown in Table 1. The total dried sludge production is found 676.7 tonne d"1 (246995.5 tonne year1). The bio-oil from algae is used after trans esterification as biodiesel and the remaining LEA is also used as protein source as discussed earlier, and 1157.05 tonne LEA year1, which accounts total earning of $0.17 million annually. The alga cultivation in FCWW

also earns carbon credit (3041.91 tonne year1) which accounts total earning of $0.03 million annually. The total credit earned by integrated process model is $9.59 million annually as shown in Table 5D. The estimated production cost of algal oil (TAG) by utilizing FCWW after coagulation as a nutrient medium was found to be $79.03 barrel"1 ($1.88 gallon1) from the system in present invention.
Nutrient (COD) and water (FCWW) balance for the plant having 10 barrels per day oil production capacity in addition to 20 MLD wastewater treatment facility. Coagulation facility needs 20169 m3 raw FCWW (887.4 tonne total COD) and 2.14 tonne chitosan per day as inputs and produced 18960 m3 pre-treated FCWW and 676.7 tonne of CFS daily. Coagulation facility removed 576.84 tonne COD per day at the rate of 269.55 tonne COD per tonne of chitosan. 1209 m3 water lost during coagulation process as CFS volume. The 18960 m3 pre-treated FCWW, atmospheric C02 and sunlight enter in to ORP facility and leads to the production of 4.63 tonne dry algal biomass and treated FCWW. The ORP system removed 184.74 tonne COD at the rate of 39.9 tonne per algal biomass whereas the remaining COD exit as unrecovered nutrients in the liquid fraction obtained from ORP system after dewatering and harvesting of algal biomass. 1.46 tonne algal oil (10 barrels), 3.17 tonne LEA was produced per day from algal biomass (4.63 tonne) produced in ORP system.

Table 5
Table 5A Capital cost involved in the proposed 20 MLD coagulation facility
and ORP plant for algal cultivation for algal oil production; Table 5B - E Cost
breakdown of developed process for its operation and revenue generation from
the products

A. Capital cost
S. No Items Unit
prices
($) Amoun
t Price require ($)
d References
1 Coagulation facility cost 125314 .10 55 50382. Guoetal.,2014
2 Earthwork ORP 916.05

hectare"1 hectares 75
3 Piping 2130.86 55 117197
hectare"1 hectares .30
4 Land 7407.41 55 407407
hectare"1 hectares .55 c--
5 Liners 74.65 4.63 345.63 o
tonne"1 tonne
6 Pump system for water 53.2 4.63 246.32 C3
CD
tonne"1 tonne s
7 Building cost 406.4 4.63 1881.6 eg
tonne"1 tonne 3 o
8 Harvesting system 251 tonne 4.63 1162.1 K
i tonne 3
9 Site development 733.33 55 40333.
hectare"1 hectares 15
10 Other cost (light, electricity 152.4 4.63 705.61
and wiring etc.) tonne"1 3000 tonne 46.3 138900
11 Hexane(10:l)

Rogers etal., 2014
tonne"1 tonne .00
Total capital coat 883876 .17
B. Operational Expenses
(OpEx)
S. Unit Amoun _ . Price
. ($ day require x. J
d )
No Items prices ($)
References
1 Flocculant (chitosan) 10000 2.14 21400. Davis etal, 2011
tonne"1 0.11 kWh" tonnes 00 1865.67 205.22
2 Onsite pumping circulation

M wi ,, w rt O O