Sustainable bioenergy for India: Technical, economic and policy analysis

India's energy challenges are multi-pronged. They are manifested through growing demand for modern energy carriers, a fossil fuel dominated energy system facing a severe resource crunch, the need for creating access to quality energy for the large section of deprived population, vulnerable energy security, local and global pollution regimes and the need for sustaining economic development. Renewable energy is considered as one of the most promising alternatives. Recognizing this potential, India has been implementing one of the largest renewable energy programmes in the world. Among the renewable energy technologies. bioenergy has a large diverse portfolio including efficient biomass stoves, biogas, biomass combustion and gasification and process heat and liquid fuels. India has also formulated and implemented a number of innovative policies and programmes to promote bioenergy technologies. However, according to some preliminary studies, the success rate is marginal compared to the potential available. This limited success is a clear indicator of the need for a serious reassessment of the bioenergy programme. Further, a realization of the need for adopting a sustainable energy path to address the above challenges will be the guiding force in this reassessment. In this paper an attempt is made to consider the potential of bioenergy to meet the rural energy needs: (I) biomass combustion and gasification for electricity; (2) biomethanation for cooking energy (gas) and electricity; and (3) efficient wood-burning devices for cooking. The paper focuses on analysing the effectiveness of bioenergy in creating this rural energy access and its sustainability in the long run through assessing: the demand for bioenergy and potential that could be created; technologies, status of commercialization and technology transfer and dissemination in India; economic and environmental performance and impacts: bioenergy policies, regulatory measures and barrier analysis. The whole assessment aims at presenting bioenergy as an integral part of a sustainable energy strategy for India. The results show that bioenergy technology (BET) alternatives compare favourably with the conventional ones. The cost comparisons show that the unit costs of BET alternatives are in the range of 15-187% of the conventional alternatives. The climate change benefits in terms of carbon emission reductions are to the tune of 110 T C per year provided the available potential of BETs are utilized.

Indicator-based urban sustainability-A review

Sustainability has emerged as one of the important planning concepts from its beginnings in economics and ecological thinking, and has widely been applied to assessing urban development. Different methods, techniques and instruments for urban sustainability assessment that help determine how cities can become more sustainable have emerged over a period of time. Among these, indicator-based approaches contribute to building of sustainable self-regulated systems that integrate development and environment protection. Hence, these provide a solid foundation for decision-making at all levels and are being increasingly used. The present paper builds on the background of the available literature and suggests the need for benchmarking indicator-based approach in a given urban area and incorporating various local issues, thus enhancing the long-term sustainability of cities which can be developed by introducing sustainability indicators into the urban planning process. (C) 2013 International Energy Initiative. Published by Elsevier Inc. All rights reserved.

Ergodic control of partially observed Markov chains

The ergodic or long-run average cost control problem for a partially observed finite-state Markov chain is studied via the associated fully observed separated control problem for the nonlinear filter. Dynamic programming equations for the latter are derived, leading to existence and characterization of optimal stationary policies.

Laser Surface Alloying of a Creep Resistant Magnesium Alloy MRI 230D with Al and Al2O3

A creep resistant permanent mould cast Mg alloy MRI 230D was laser surface alloyed with Al and a mixture of Al and Al2O3 using pulsed Nd:YAG laser irradiation at four different scan speeds in order to improve the corrosion and wear resistance. The microstructure, corrosion and wear behavior of the laser surface alloyed material is reported in this manuscript. The coating comprised of a featureless microstructure with cellular-dendritic microstructure near the interface and exhibited good interfacial bonding. A few solidification cracks reaching down to substrate were also observed. The two step coating with Al followed by a mixture of Al and Al2O3 exhibited a slightly better corrosion resistance than the single step coating with Al. In the long run, however, corrosion resistance of both the coatings became comparable to the as-cast alloy. The corroded surface of the laser surface alloyed specimens revealed a highly localized corrosion. The laser surface alloyed specimens exhibited an improvement in wear resistance. The laser scan speed did not exhibit a monotonic trend either in corrosion or wear resistance.

Corona Aging Studies on Silicone Rubber Nanocomposites

In EHV and UHV power transmission lines, corona could occur even on well designed transmission line hardware and insulators especially under wet conditions. Corona if allowed to occur continuously can significantly damage the polymeric insulators used in such lines in the long run. This paper presents the experimental results of corona aging studies conducted on unfilled silicone rubber as well as filled silicone rubber nanocomposites. Corona aging studies were conducted on silicone rubber samples with filler concentrations of 0, 1, 2 and 3 % by wt of nanosilica for 25 h and 50 h. Needle-plane electrode geometry has been used to create the corona on the samples. Different characterization techniques such as Scanning Electron Microscopy, Energy Dispersive X-ray analysis, Hydrophobicity, Fourier Transform Infrared Spectroscopy, and Optical Profilometry have been used to assess the relative performance of the samples with respect to corona aging. Results indicate that at 3 wt %, the performance of the nanocomposite is much better than the unfilled silicon rubber which can be attributed to the modifications in the material caused by the size factor of the filler.

Stochastic Approximation Algorithms for Constrained Optimization via Simulation

We develop four algorithms for simulation-based optimization under multiple inequality constraints. Both the cost and the constraint functions are considered to be long-run averages of certain state-dependent single-stage functions. We pose the problem in the simulation optimization framework by using the Lagrange multiplier method. Two of our algorithms estimate only the gradient of the Lagrangian, while the other two estimate both the gradient and the Hessian of it. In the process, we also develop various new estimators for the gradient and Hessian. All our algorithms use two simulations each. Two of these algorithms are based on the smoothed functional (SF) technique, while the other two are based on the simultaneous perturbation stochastic approximation (SPSA) method. We prove the convergence of our algorithms and show numerical experiments on a setting involving an open Jackson network. The Newton-based SF algorithm is seen to show the best overall performance.

Optimal management of beaver population using a reduced-order distributed parameter model and single network adaptive critics

Beavers are often found to be in conflict with human interests by creating nuisances like building dams on flowing water (leading to flooding), blocking irrigation canals, cutting down timbers, etc. At the same time they contribute to raising water tables, increased vegetation, etc. Consequently, maintaining an optimal beaver population is beneficial. Because of their diffusion externality (due to migratory nature), strategies based on lumped parameter models are often ineffective. Using a distributed parameter model for beaver population that accounts for their spatial and temporal behavior, an optimal control (trapping) strategy is presented in this paper that leads to a desired distribution of the animal density in a region in the long run. The optimal control solution presented, imbeds the solution for a large number of initial conditions (i.e., it has a feedback form), which is otherwise nontrivial to obtain. The solution obtained can be used in real-time by a nonexpert in control theory since it involves only using the neural networks trained offline. Proper orthogonal decomposition-based basis function design followed by their use in a Galerkin projection has been incorporated in the solution process as a model reduction technique. Optimal solutions are obtained through a "single network adaptive critic" (SNAC) neural-network architecture.

Locating and sizing plants for bottling propane in south India

Shri Shakti LPG Ltd. (SSLPG) imports and markets propane (referred to as liquefied petroleum gas (LPG) in India) in south India. It sells LPG in packed (cylinder) form to domestic customers and commercial establishments through a network of dealers. Dealers replenish their stocks of filled cylinders from bottling plants, which in turn receive LPG in bulk from the cheaper of SSLPG's two import-and-storage facilities that are located on the Indian coast. We implemented integer programming to help SSLPG decide on the locations and long-run sizes of its bottling plants. We estimate that our recommended configuration of bottling plants is about $1 million cheaper annually than the one that SSLPG had initially planned.

Stochastic Algorithms for Discrete Parameter Simulation Optimization

We present two efficient discrete parameter simulation optimization (DPSO) algorithms for the long-run average cost objective. One of these algorithms uses the smoothed functional approximation (SFA) procedure, while the other is based on simultaneous perturbation stochastic approximation (SPSA). The use of SFA for DPSO had not been proposed previously in the literature. Further, both algorithms adopt an interesting technique of random projections that we present here for the first time. We give a proof of convergence of our algorithms. Next, we present detailed numerical experiments on a problem of admission control with dependent service times. We consider two different settings involving parameter sets that have moderate and large sizes, respectively. On the first setting, we also show performance comparisons with the well-studied optimal computing budget allocation (OCBA) algorithm and also the equal allocation algorithm. Note to Practitioners-Even though SPSA and SFA have been devised in the literature for continuous optimization problems, our results indicate that they can be powerful techniques even when they are adapted to discrete optimization settings. OCBA is widely recognized as one of the most powerful methods for discrete optimization when the parameter sets are of small or moderate size. On a setting involving a parameter set of size 100, we observe that when the computing budget is small, both SPSA and OCBA show similar performance and are better in comparison to SFA, however, as the computing budget is increased, SPSA and SFA show better performance than OCBA. Both our algorithms also show good performance when the parameter set has a size of 10(8). SFA is seen to show the best overall performance. Unlike most other DPSO algorithms in the literature, an advantage with our algorithms is that they are easily implementable regardless of the size of the parameter sets and show good performance in both scenarios.

An Online Actor-Critic Algorithm with Function Approximation for Constrained Markov Decision Processes

We develop an online actor-critic reinforcement learning algorithm with function approximation for a problem of control under inequality constraints. We consider the long-run average cost Markov decision process (MDP) framework in which both the objective and the constraint functions are suitable policy-dependent long-run averages of certain sample path functions. The Lagrange multiplier method is used to handle the inequality constraints. We prove the asymptotic almost sure convergence of our algorithm to a locally optimal solution. We also provide the results of numerical experiments on a problem of routing in a multi-stage queueing network with constraints on long-run average queue lengths. We observe that our algorithm exhibits good performance on this setting and converges to a feasible point.

Virtually cool ternary content addressable memory

Fast content addressable data access mechanisms have compelling applications in today's systems. Many of these exploit the powerful wildcard matching capabilities provided by ternary content addressable memories. For example, TCAM based implementations of important algorithms in data mining been developed in recent years; these achieve an an order of magnitude speedup over prevalent techniques. However, large hardware TCAMs are still prohibitively expensive in terms of power consumption and cost per bit. This has been a barrier to extending their exploitation beyond niche and special purpose systems. We propose an approach to overcome this barrier by extending the traditional virtual memory hierarchy to scale up the user visible capacity of TCAMs while mitigating the power consumption overhead. By exploiting the notion of content locality (as opposed to spatial locality), we devise a novel combination of software and hardware techniques to provide an abstraction of a large virtual ternary content addressable space. In the long run, such abstractions enable applications to disassociate considerations of spatial locality and contiguity from the way data is referenced. If successful, ideas for making content addressability a first class abstraction in computing systems can open up a radical shift in the way applications are optimized for memory locality, just as storage class memories are soon expected to shift away from the way in which applications are typically optimized for disk access locality.

Transmit power Control with ARQ in energy harvesting sensors: a decision-theoretic approach

This paper addresses the problem of finding optimal power control policies for wireless energy harvesting sensor (EHS) nodes with automatic repeat request (ARQ)-based packet transmissions. The EHS harvests energy from the environment according to a Bernoulli process; and it is required to operate within the constraint of energy neutrality. The EHS obtains partial channel state information (CSI) at the transmitter through the link-layer ARQ protocol, via the ACK/NACK feedback messages, and uses it to adapt the transmission power for the packet (re)transmission attempts. The underlying wireless fading channel is modeled as a finite state Markov chain with known transition probabilities. Thus, the goal of the power management policy is to determine the best power setting for the current packet transmission attempt, so as to maximize a long-run expected reward such as the expected outage probability. The problem is addressed in a decision-theoretic framework by casting it as a partially observable Markov decision process (POMDP). Due to the large size of the state-space, the exact solution to the POMDP is computationally expensive. Hence, two popular approximate solutions are considered, which yield good power management policies for the transmission attempts. Monte Carlo simulation results illustrate the efficacy of the approach and show that the approximate solutions significantly outperform conventional approaches.

Advancing biodiversity research in developing countries: the need for changing paradigms

The world is in the midst of a biodiversity crisis, threatening essential goods and services on which humanity depends. While there is an urgent need globally for biodiversity research, growing obstacles are severely limiting biodiversity research throughout the developing world, particularly in Southeast Asia. Facilities, funding, and expertise are often limited throughout this region, reducing the capacity for local biodiversity research. Although western scientists generally have more expertise and capacity, international research has sometimes been exploitative ``parachute science,'' creating a culture of suspicion and mistrust. These issues, combined with misplaced fears of biopiracy, have resulted in severe roadblocks to biodiversity research in the very countries that need it the most. Here, we present an overview of challenges to biodiversity research and case studies that provide productive models for advancing biodiversity research in developing countries. Key to success is integration of research and education, a model that fosters sustained collaboration by focusing on the process of conducting biodiversity research as well as research results. This model simultaneously expands biodiversity research capacity while building trust across national borders. It is critical that developing countries enact policies that protect their biodiversity capital without shutting down international and local biodiversity research that is essential to achieve the long-term sustainability of biodiversity, promoting food security and economic development.

Adaptive Sleep-Wake Control using Reinforcement Learning in Sensor Networks

The aim in this paper is to allocate the `sleep time' of the individual sensors in an intrusion detection application so that the energy consumption from the sensors is reduced, while keeping the tracking error to a minimum. We propose two novel reinforcement learning (RL) based algorithms that attempt to minimize a certain long-run average cost objective. Both our algorithms incorporate feature-based representations to handle the curse of dimensionality associated with the underlying partially-observable Markov decision process (POMDP). Further, the feature selection scheme used in our algorithms intelligently manages the energy cost and tracking cost factors, which in turn assists the search for the optimal sleeping policy. We also extend these algorithms to a setting where the intruder's mobility model is not known by incorporating a stochastic iterative scheme for estimating the mobility model. The simulation results on a synthetic 2-d network setting are encouraging.

Energy Sharing for Multiple Sensor Nodes With Finite Buffers

We consider the problem of finding optimal energy sharing policies that maximize the network performance of a system comprising of multiple sensor nodes and a single energy harvesting (EH) source. Sensor nodes periodically sense the random field and generate data, which is stored in the corresponding data queues. The EH source harnesses energy from ambient energy sources and the generated energy is stored in an energy buffer. Sensor nodes receive energy for data transmission from the EH source. The EH source has to efficiently share the stored energy among the nodes to minimize the long-run average delay in data transmission. We formulate the problem of energy sharing between the nodes in the framework of average cost infinite-horizon Markov decision processes (MDPs). We develop efficient energy sharing algorithms, namely Q-learning algorithm with exploration mechanisms based on the epsilon-greedy method as well as upper confidence bound (UCB). We extend these algorithms by incorporating state and action space aggregation to tackle state-action space explosion in the MDP. We also develop a cross entropy based method that incorporates policy parameterization to find near optimal energy sharing policies. Through simulations, we show that our algorithms yield energy sharing policies that outperform the heuristic greedy method.