Enhancement of growth and biomolecules (carbohydrates, proteins, and chlorophylls) of isolated Chlorella thermophila using optimization tools.

The production of multiple products from microalgae is essential for economic sustainability and the knowledge of optimum cultivation conditions for high growth and biomolecule synthesis of a microalgal strain is the prerequisite for its commercial production. In this work, optimization of nutrient concentrations for the cultivation of isolated Chlorella thermophila was performed by manipulating nine nutrients with the objectives of maximization of growth, carbohydrate, protein, and chlorophyll contents. Experiments were designed and effects of the parameters were studied using Taguchi orthogonal array (TOA). Experimental results of TOA were used for modeling artificial neural networks (ANN) followed by the optimization using genetic algorithm (GA) to find global optimal solutions. Results showed an increase of 36, 88, 36, and 88% for growth, carbohydrates, proteins, and chlorophylls, respectively, at optimal combinations of parameters given by TOA. Results obtained through the ANN-GA optimization were 9, 10, and 3% more compared to the TOA for biomass, carbohydrates, and chlorophylls, respectively with experimental verification. Nitrates and bicarbonate were found to play the most pivotal role in biomass and biomolecule synthesis of the isolated microalgal strain. Results of the current investigation can be used in the industrial scale-up for the production of multiple products using the biorefinery approach.

Integrated approach for optimal sensor placement and state estimation: A case study on water distribution networks.

The objective of the design and operation of any water distribution network (WDN) includes meeting the desired demand at sufficient pressure at all nodes. However, this requires situational awareness; in other words, the knowledge of system state variables such as pressure and flow throughout the network. In this work, a hybrid approach is developed for sensor placement (SP) and state estimation (SE) that exploits the underlying correlation structure in the data, along with the principles governing the flow through circular pipes. The problem of SP in WDN is addressed since measuring the state variables throughout the network is not practical. The problem of SE that maps to a matrix completion problem under certain physical and logical constraints is solved later. The completed matrix represents the state of WDN at any given time. Benchmark networks used in literature were used to evaluate the proposed approach. The mean absolute percentage error (MAPE) of less than 5% was obtained while estimating the head available at nodes. The knowledge of the states in the entire network could help operate the network adaptively.