Paul Kalb

Sulfur polymer solidification/stabilization of elemental mercury waste.

Elemental mercury, contaminated with radionuclides, presents a waste disposal problem throughout the Department of Energy complex. In this paper we describe a new process to immobilize elemental mercury wastes, including those contaminated with radionuclides, in a form that is non-dispersible, will meet EPA leaching criteria, and has low mercury vapor pressure. In this stabilization and solidification process, elemental mercury is combined with an excess of powdered sulfur polymer cement (SPC) and sulfide additives in a mixing vessel and heated to approximately 40 degrees C for several hours, until all of the mercury is converted into mercuric sulfide (HgS). Additional SPC is then added and the temperature of the mixture raised to 135 degrees C, resulting in a molten liquid which is poured into a mold where it cools and solidifies. The final treated waste was characterized by powder X-ray diffraction and found to be a mixture of the hexagonal and orthorhombic forms of mercuric sulfide. The Toxicity Characteristic Leaching Procedure was used to assess mercury releases, which for the optimized process averaged 25.8 microg/l, with some samples being well below the new EPA Universal Treatment Standard of 25 microg/l. Longer term leach tests were also conducted, indicating that the leaching process was dominated by diffusion. Values for the effective diffusion coefficient averaged 7.6x10(-18) cm2/s. Concentrations of mercury vapor from treated waste in equilibrium static headspace tests averaged 0.6 mg/m3.

3D cloud detection and tracking for solar forecast using multiple sky imagers

Cloud detection and tracking (CDT) is the most challenging problem in integrating solar energy into the smart grid. In this paper, we present a novel 3D cloud detection and tracking using images from three TSI (Total Sky Imager), and propose to incorporate history into a multi-layer cloud detection pipeline. Our pilot study shows that the new CDT significantly improves the short-term solar irradiance forecasting and enable regional radiation prediction, which is impossible with a single TSI.

Building the Sun4Cast System: Improvements in Solar Power Forecasting

AbstractAs integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from the public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers. The project followed a value chain approach to determine key research and technology needs to reach desired results.Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynamic Integrated Fo...

A Stochastic Framework for Solar Irradiance Forecasting Using Condition Random Field

Solar irradiance volatility is a major concern in integrating solar energy micro-grids to the mainstream energy power grid. Accounting for such fluctuations is challenging even with supplier coordination and smart-grid structure implementation. Short-term solar irradiance forecasting is one of the crucial components for maintaining a constant and reliable power output. We propose a novel stochastic solar prediction framework using Conditional Random Fields. The proposed model utilizes features extracted from both cloud images taken by Total Sky Imagers and historical statistics to synergistically reduce the prediction error by \(25\)-\(40\%\) in terms of MAE in \(1\)-\(5\) minute forecast experiments over the baseline methods.

Solar irradiance forecasting using multi-layer cloud tracking and numerical weather prediction

The advances in photovoltaic technology make solar energy one of the top three renewable energy sources. However, predicting the variability of solar penetration caused by cloud cover is the biggest hurdle for the effective use of solar energy. Grid operators enforce regulations that require ramp events to be within a certain range, which makes short term forecasting essential. The Total Sky Imager (TSI) is one of the best instruments for accurate short-term irradiance forecasting but is limited to a forecast of approximately five minutes for low altitude clouds, which usually cause large ground irradiance fluctuations. To extend the forecasting horizon to 15 minutes, we propose to incorporate NWP (Numerical Weather Prediction) based weather categories (every 15 minutes) into a short-term irradiance forecasting model. This advanced Support Vector Regression (SVR) is the product of our novel multilayer cloud image processing pipeline, which can handle complex cloud scenarios. We observe an average of 21% improvement over the baseline model in our systematic validations for 1-15 minute forecasts.

Sensor network based solar forecasting using a local vector autoregressive ridge framework

The significant improvements and falling costs of photovoltaic (PV) technology make solar energy a promising resource, yet the cloud induced variability of surface solar irradiance inhibits its effective use in grid-tied PV generation. Short-term irradiance forecasting, especially on the minute scale, is critically important for grid system stability and auxiliary power source management. Compared to the trending sky imaging devices, irradiance sensors are inexpensive and easy to deploy but related forecasting methods have not been well researched. The prominent challenge of applying classic time series models on a network of irradiance sensors is to address their varying spatio-temporal correlations due to local changes in cloud conditions. We propose a local vector autoregressive framework with ridge regularization to forecast irradiance without explicitly determining the wind field or cloud movement. By using local training data, our learned forecast model is adaptive to local cloud conditions and by using regularization, we overcome the risk of overfitting from the limited training data. Our systematic experimental results showed an average of 19.7% RMSE and 20.2% MAE improvement over the benchmark Persistent Model for 1-5 minute forecasts on a comprehensive 25-day dataset.

Developing EnviroSuite Resources at the National Synchrotron Light Source

The objective of Brookhaven National Laboratorys EnviroSuite Initiative is to develop the facilities, user support infrastructure, and techniques necessary to conduct world-class molecular environmental science research at the NSLS. This is intended to benefit the research of ERSD-supported scientists, both through direct access and assistance and through the indirect benefits of a broader network of environmental scientists as collaborators and users. Much of the EnviroSuite research involves close collaboration with members of the Center for Environmental Molecular Science (CEMS), an EMSI based at BNL and nearby Stony Brook University and jointly supported by ERSD (Project 1023761, P. Kalb) and NSF. This offers unique opportunities to benefit from both national laboratory facilities and university resources. Other collaborators, from around the US and the world, investigate various aspects of the underlying molecular-scale processes in complex natural systems. In general, synchrotron techniques are ideal for studying the molecular-scale structures, chemical/physical interactions, and transformations that govern the macroscopic properties and processes (e.g. transport, bioavailability) of contaminants in the environment. These techniques are element-specific, non-destructive, and sensitive to the very low concentrations found in real-world samples.