Bringing sustainable innovative solutions/ideas to a problem
We are over 7.4 billion people that live on this small blue planet. We have limited resources. Yes, there are boundaries, but we share air, water, the environment. Our growing energy needs are driving us towards both increasing the production and also finding alternate solutions.
Our prime concern these days towards design thinking should be directed towards environment because solutions we will bring up or have in future are on this planet or will emanate from this planet.
Just think about this idea – “If we place SOx, NOx, CO measurement system in a vehicle and place a control system in the vehicle such a way that if the pollution is more than the prescribed limits of vehicular emission a timer say 200 hours will start and if the driver/vehicle owner doesn’t get things in order the vehicle won’t get started after completion of 200 hours. In this way we will remove polluting vehicles of the road.”
Our work and research towards finding solutions to a problem if are not directed with an aim of sustainability in mind, then there will be more problems in future than there are now. It is the concept of Design Thinking that comes into picture. Without any previous set of data or analytics to a problem, one tends to find a solution by taking interviews of those affected, taking expert opinions and then come up with a solution.
Thomas Robert Mathus theory, “Principle of Population” proposed that human population grows exponentially,while food production grows at an arithmetic rate.
Malthus theory has been questioned by many and there exist many critique for the same. Malthus theory’s final result targets towards the available food for the population, but in reality this theory needs revision, there needs to be added other factors.
Population Growth+Energy Demand+Environmental Degradation together will bring the final output i.e. decreased availability of quality and real food.
Quality & Real Food, is it really available today, genetic research has led to genetically modified crops, which are drought and bug resistant, need less water; this all sounds good, but what about the production and distribution.
Further, these days there are lot of synthetic food products in the market, so no quality food here.
The basic concept of carbon storing is simple i.e. catch carbon dioxide from factories and other industrial facilities before it goes into the atmosphere and then either store it indefinitely underground or inject it into oil reservoirs to help pump out more oil.
But while many experts have touted the process as an essential factor in the mitigation of climate change, others have argued that it’s too risky.
A new study published in the journal Nature Communications has addressed a concern associated with carbon storage i.e. safety. In the past, critics have suggested that carbon dioxide stored underground may be able to corrode the rock layers above it and eventually escape, a possibility that’s been supported by some modeling and laboratory studies. This would be bad for the climate, of course, but some environmental and public health advocates have also worried that escaped carbon dioxide in large volumes could damage the water or air quality of nearby communities. But the new study suggests that such concerns may be overblown. The researchers examined a natural carbon dioxide reservoir near Green River, Utah, and found that the carbon dioxide has been trapped underground there for about 100,000 years without dangerously corroding the rocks that are trapping it in place. (For perspective, climate experts have suggested that carbon dioxide must be kept stored underground for at least 10,000 years to keep it from adding to the current global warming.
These observations suggest that storing carbon underground may (at least at some sites) be much safer than previous model and laboratory experiments have suggested.
Human intelligence is unparallel and it is using this intelligence we are working towards the concept of artificial intelligence, we are achieving success, still a lot of research and activities are going on in this field. There are various schemes that scientists and enthusiasts all over the world are working on, these are Neural Networks, Fuzzy logic and genetic algorithms.
Neural Networks involve a learning mechanism which is explained in manner the human brain works. Fuzzy logic can be seen as the principle basis used by various companies to develop day to day machines like washing machine, air conditioners. Genetic algorithms if we think of is more of an optimization.
Genetic algorithms if we think of is more of an optimization. It uses the best parameters in a set of events, outcomes and results and extracts the best result out of it. It is based on concept of Human evolution i.e, the way human species developed and evolved with passing time.[GeneticAlgorithms.ppt] .
A Genetic Algorithm (GA) is a robust optimization technique based on natural selection. The basic goal of GAs is to optimize functions called fitness functions. GA-based approaches differ from conventional problem-solving methods in several ways. First, GAs work with a coding of the parameter set rather than the parameters themselves. Second, GAs search from a population of points rather than a single point. Third, GAs use payoff (objective function) information, not other auxiliary knowledge. Finally, GAs use probabilistic transition rules, not deterministic rules. These properties make GAs robust, powerful, and data-independent.
Its basis in natural selection allows a GA to employ a “survival of the fittest” strategy when searching for optima. The use of a population of points helps the GA avoid converging to false peaks (local optima) in the search space.
Chromosome: A simple GA requires the parameter set of the optimization problem to be encoded as a string (binary, real, etc.). These strings are known as chromosomes. They are manipulated by the GA in an attempt to obtain the string that represents the optimal solution to the problem. Evolutionary process works on chromosomes, i.e., chromosomes are used to pass information from one generation to the next and also for information exchange between two individuals of the same generation to create a new individual consisting of a combination of information from both the parent individuals.
Genes: A character or symbol in a GA chromosome is called as a gene. Genes are the basic building blocks of the solution and represent the properties which make one solution different from the other.
Allele: The value of a gene in a GA is called an allele, such as for eye color, the different possible ‘settings’ (e.g., blue, brown, hazel etc.) are called alleles.
Selection: A genetic operator used to select individuals for reproduction .
Crossover: A key operator used in the GA to create new individuals by combining portions of two parent strings .
Crossover probability: Probability of performing a crossover operation, denoted by pc, i.e., the ratio of the number of offspring produced in each generation to the population size. This value of pc is chosen generally in the range of 0.7 to 0.9.
Mutation: An incremental change to a member of the GA population .
Mutation Probability: The probability of mutating each gene in a GA chromosome, denoted by pm. This value is chosen generally in the range of 0.01 to 0.03.
B GA Basics
A simple GA starts with a population of solutions encoded in one of the many ways. Binary encodings are quite common and are used in this thesis. The GA determines each string’s strength based on an objective function and performs one or more of the three genetic operators on certain strings in the population , .
repeat repeat select a pair of members randomly perform crossover on the two members perform mutation on the crossed members insert the new members into the population until done with current generation until done with GA
For Publications on genetic algorithms, go to Home Page for download
A few years back power houses generated power and the grids distributed it. There used to occur load shedding based on peak demand and if there used to be any scope power generated was ramped up.
Simple Grids to supply us the power energy to run our homes, our industries.
Today we are working day and night towards finding other sources/alternatives for power generation and unlike the power few years back this power is decentralized and hence the need for smart and new breed grids.
One major challenge associated with renewable energy is that the power is dependent on weather conditions, if the conditions are conducive we get lots of power. Now unlike our conventional power plants this creates a problem. Hence, smart grids with power storage is of extreme importance today.