CHARACTERIZING AND IMPROVING PRODUCTION OF FERMENTABLE SUGARS AND CO-PRODUCTS FROM A FOREST PRODUCT INDUSTRY WASTEWATER STREAM

Hardboard processing wastewater was evaluated as a feedstock in a bio refinery co-located with the hardboard facility for the production of fuel grade ethanol. A thorough characterization was conducted on the wastewater and the composition changes of which during the process in the bio refinery were tracked. It was determined that the wastewater had a low solid content (1.4%), and hemicellulose was the main component in the solid, accounting for up to 70%. Acid pretreatment alone can hydrolyze the majority of the hemicellulose as well as oligomers, and over 50% of the monomer sugars generated were xylose. The percentage of lignin remained in the liquid increased after acid pretreatment. The characterization results showed that hardboard processing wastewater is a feasible feedstock for the production of ethanol. The optimum conditions to hydrolyze hemicellulose into fermentable sugars were evaluated with a two-stage experiment, which includes acid pretreatment and enzymatic hydrolysis. The experimental data were fitted into second order regression models and Response Surface Methodology (RSM) was employed. The results of the experiment showed that for this type of feedstock enzymatic hydrolysis is not that necessary. In order to reach a comparatively high total sugar concentration (over 45g/l) and low furfural concentration (less than 0.5g/l), the optimum conditions were reached when acid concentration was between 1.41 to 1.81%, and reaction time was 48 to 76 minutes. The two products produced from the bio refinery were compared with traditional products, petroleum gasoline and traditional potassium acetate, in the perspective of sustainability, with greenhouse gas (GHG) emission as an indicator. Three allocation methods, system expansion, mass allocation and market value allocation methods were employed in this assessment. It was determined that the life cycle GHG emissions of ethanol were -27.1, 20.8 and 16 g CO2 eq/MJ, respectively, in the three allocation methods, whereas that of petroleum gasoline is 90 g CO2 eq/MJ. The life cycle GHG emissions of potassium acetate in mass allocation and market value allocation method were 555.7 and 716.0 g CO2 eq/kg, whereas that of traditional potassium acetate is 1020 g CO2/kg.

Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as f-test is performed during each node’s split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.

Improving IT process management through value stream mapping approach: A case study

Value-Stream mapping (VSM) is a helpful tool to identify waste and improvement areas. It has emerged as a preferred way to support and implement the lean approach. While lean principles are well-established and have broad applicability in manufacturing, their extension to information technology is still limited. Based on a case study approach, this paper presents the implementation of VSM in an IT firm as a lean IT improvement initiative. It involves mapping the current activities of the firm and identifying opportunities for improvement. After several interviews with employees who are currently involved in the process, current state map is prepared to describe the existing problem areas. Future state map is prepared to show the proposed improvement action plans. The achievements of VSM implementation are reduction in lead time, cycle time and resources. Our finding indicates that, with the new process change, total lead time can be reduced from 20 days to 3 days – 92% reduction in overall lead time for database provisioning process.

Stream water‐quality monitoring conducted in support of the Iowa Nutrient Reduction Strategy (2016)

Monitoring of nitrogen and phosphorus in streams and rivers throughout Iowa is an essential element of the Iowa Nutrient Reduction Strategy (INRS). Sampling and analysis of surface water is necessary to develop periodic estimates of the amounts of nitrogen and phosphorus transported from Iowa. Surface and groundwater monitoring provides the scientific evidence needed to document the effectiveness of nutrient reduction practices and the impact they have on water quality. Lastly, monitoring data informs decisions about where and how best to implement nutrient reduction practices, by both point sources and nonpoint sources, to provide the greatest benefit at the least cost. The impetus for this report comes from the Water Resources Coordination Council (WRCC) which states in its 2014‐15 Annual Report “Efforts are underway to improve understanding of the multiple nutrient monitoring efforts that may be available and can be compared to the nutrient WQ monitoring framework to identify opportunities and potential data gaps to better coordinate and prioritize future nutrient monitoring efforts.” This report is the culmination of those efforts.

Iowa Nutrient Reduction Strategy stream water quality monitoring in Iowa : measuring progress (2016)

The Iowa Nutrient Reduction Strategy (NRS) is a research- and technology-based approach to assess and reduce nutrients—nitrogen and phosphorus—delivered to Iowa waterways and the Gulf of Mexico by 45 percent. To measure progress, researchers track many different factors, from inputs (e.g. funding) and the human domain (e.g. farmer perspectives) to land management (e.g. on-farm practices) and water quality. Monitoring Iowa streams provides valuable insight into measuring water quality progress and the reduction of surface water nutrient loss. The Iowa Nutrient Reduction Strategy (NRS) aims to reduce the load, or total amount (e.g. tons), of nutrients lost annually. Researchers calculate the load from water monitoring results, which measure concentration combined with stream flow.

Land & Stream Management, June 2, 2010

Iowa’s rivers are constantly shifting and changing and can be challenging places to design, construct, and maintain water trails. This section discusses aspects you will immediately encounter when developing a water trail: launches, parking areas, and trails. The intended users and expected use suggest how these amenities are designed and constructed. Water trails intended for extended families, for example, are designed differently from those intended for experienced paddlers on multi-day trips.