The in–depth investigation of a physical and chemical phenomenon occurring in a process environment is the key to develop a successful design. The integrated biological system with the mathematical and statistical network would not only enhance the production yield but also reduces the overall production cost and time required for algae cultivation.
Figure 2.1: Various optimization methods used for response prediction.
Traditionally, optimization of single influential factor one at a time is usually performed during microalgae cultivation. However, the fact is, that the response of microalgae is a product of various physicochemical factors. Therefore, it is difficult to decide that among all the selected parameters which one is most significantly affecting the microalgae behavior. Moreover, single factor optimization is time–consuming, tedious process and gives erroneous results (Marudhupandi et al., 2016). Therefore, advanced integrated process system has been adopted to improve production yield of microalgae oriented products.
The advantage of integrated process system is that it accurately defines the problem in the form of the mathematical model and therefore provide a pool of best suitable
alternative responses (Seferlis and Georgiadis, 2004). In the algae culture system, RSM coupled with central composite design (CCD) is popularly used as a tool to model the probable curvatures as measured responses and provides good fitting to the linear responses (Saeidi et al., 2016). Additionally, RSM was used as a decision–making tool for optimization of culture conditions of green microalgae Chlorella sp. for biodiesel production where nitrate, phosphate, glucose, and pH of the medium are considered as effective parameters for lipid induction in microalgae. The most significant increase in lipid production (dry cell weight basis) occurred at limited concentrations of nitrate and phosphate, 1 % (wt.) glucose and pH 7.5. The addition of nitrates during the mid–lag and mid–exponential phase showed the maximum inhibitory effect on lipid accumulation and the presence of yeast extract led to a further enhancement of lipid accumulation. Among all the tested media, BG–11 was found to be the best medium for chlorophyll content and algal biomass production. A significant increase in algal biomass was observed in BG–11 supplemented with bicarbonate and 1 % (wt.) glucose (Kirrolia et al., 2014).
Cheng et al. (2013) have also done the statistical optimization of culture media for growth and lipid production in Chlorella protothecoides UTEX250. The concentration of NaNO3, MgSO4.7H2O, and proteose, in the culture medium, was optimized by RSM using Box–Behnken design (BBD). The optimal concentrations found were 0.45 g/L of NaNO3, 6 mg/L of MgSO4.7H2O, and 0.25 g/L of proteose for maximum biomass production of 1.19 g/L and 2 mg/L of MgSO4.7H2O and no addition of NaNO3 and proteose for lipid accumulation. At the optimal condition, biomass concentration obtained was 1.19 g/L, which was 1.8 times higher than obtained in the original medium. Whereas, in lipid production medium a lipid content obtained was 12.9 % dcw, which was three times higher than of original medium. The fatty acid profile of
algae grown in the optimized medium demonstrated a higher unsaturated fatty acid content of methyl linoleate (C18:2) and methyl linolenate (C18:3) than that of the algae grown in an original medium.
Although, RSM has been popularly used as a statistical and mathematical design tool.
However, the limitations of RSM technique is that it requires some preliminary experiments to set the selection range, cannot predict non–linear relationship and response for the unseen data. Whereas, ANN can predict response to the unseen data and therefore, is being progressively applied in various bioprocess optimization systems to overcome the problems associated with RSM for both linear and non–linear responses (del Rio–Chanona et al., 2016).
ANN is the highly interconnected network of processing elements (neurons) capable of massive parallel computations, representing a data–centric modeling inspired by the biological nervous system. It fundamentally transforms the inputs that passes through a network of neurons with weighed interconnection into the output, predicted to the best of its ability (Ahmad et al., 2013). ANN is more flexible and does not impose any restriction on the type of relationship governing the dependence output parameters on various running conditions (Garcíaa‐Gimeno et al., 2003). It can also perform a task which is unpredictable by linear programming. Therefore, in the last two decades for non–linear multivariate modeling ANN proved to be an effective tool. Conceptually, ANN requires a huge number of data sets for the model fitting, thus the data generated by RSM should be sufficient to build an effective ANN network.
In a comparative study for optimization of biodiesel from Mahua oil using RSM and ANN, ANN offered better prediction capability compared to RSM. It was also suggested that ANN can be chosen to optimize the process parameters and can accurately explain the non–linear relationship (Sarve et al., 2015).
Furthermore, Maran and Priya (2015) have selected RSM and ANN optimization for ultrasound–mediated intensification of biodiesel production in neem and muskmelon oil. They have observed high response prediction capability of ANN compared to RSM.
Most of the optimization studies carried out so far are primarily on downstream processing of microalgae biodiesel. Still, limited reports are available in the literature, where, RSM and ANN models are developed and studied together for the optimization of the upstream process of microalgae culture conditions for biofuel production (Dineshkumar et al., 2015).
Uptill now, most of the biodiesel production optimization studies primarily conducted using RSM and ANN based approaches. Nevertheless, the problem with these two optimization methods (RSM and ANN) is their local approximation generalization.
However, the individual experimental setup for local estimation is not feasible for the process system engineering due to the huge expense of energy and time. Therefore, GA is one such technique to provide a global optimum condition. It is a prerequisite for developing optimization model to look after local optimization problems. Furthermore, the genetic algorithm provides a condition for global optimization for both linear and non–linear programming. GA is used for stochastic optimization with an aim to minimize the objective function based on Darwinian Theory of “survival of the fittest”
(Li et al., 2013). It creates solutions for the problem available in search space using its genetic operator, selection, crossing over and mutation (Fayyazi et al., 2015). In the recent years, GA based on RSM and ANN models (as an objective function) have been applied successfully to optimize the input space of a bioprocess where RSM and ANN generated second–order polynomial equation was used as a fitness function (Muthuraj et al., 2015; Prabhu and Jayadeep, 2016).
A genetic algorithm is not function optimization technique but it is a simple and powerful general purpose stochastic optimization. The main advantage of GA is that it uses stochastic operators instead of deterministic rules to reach the solution.
Furthermore, GA considers many points in the search space simultaneously in–spite of determining a single point thereby reducing the chance of converging to local minima (Fayyazi et al., 2015). The input data processed by GA is used to create an initial population (set of a possible solution) either randomly or heuristically. The structure of the population is chosen by a randomized selection process which ensures that the expected number of times a structure chosen is approximately equal to the structure’s performance (fitness) relative to the rest of the population. The less fit individuals in the population die during the selection process. Further, to search other points in space some variations are introduced into the new population by using idealized recombination (translational) operators (eg. Crossovers, mutation). Therefore, over the generation, the best fit individual population survives and the best suitable optimized condition is generated globally (Kadiyala et al., 2010).
Hence, from the above discussion it is proposed that computational intelligence techniques (artificial intelligence) are efficient modeling tools used in optimization, process intensification, supervision and control of biodiesel production centered on global optimization method. Nowadays, the process system engineering becomes essential for establishing sustainable and less energy intensive method (Nasir et al., 2013; Nicoletti et al., 2009).