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Ready access to comprehensive influent information can help water reclamation plant (WRP) operators implement better real-time process controls, provide operational reliability and reduce energy consumption. The five-day biochemical oxygen demand (BOD 5 ), a critical parameter for WRP process control, is expensive and difficult to measure using hard-sensors. An alternative approach based on a soft-sensor methodology shows promise, but can be problematic when used to predict high BOD 5 values. Underestimating high BOD 5 concentrations for process control could result in an insufficient amount of aeration, increasing the risk of an effluent violation. To address this issue, we tested a hierarchical hybrid soft-sensor approach involving multiple linear regression, artificial neural networks (ANN), and compromise programming. While this hybrid approach results in a slight decrease in overall prediction accuracy relative to the approach based on ANN only, the underestimation percentage is substantially lower (37% vs. 61%) for predictions of carbonaceous BOD 5 (CBOD 5 ) concentrations higher than the long-term average value. The hybrid approach is also flexible and can be adjusted depending on the relative importance between energy savings and managing the risk of an effluent violation. [ABSTRACT FROM AUTHOR]

Prediction of natural gas demand can help to better manage energy demand and supply and recent developments of deep learning methods make it possible to improve forecast performance. This study examined the feasibility of hourly natural gas demand forecast, and compared statistical and deep learning methods to evaluate their prediction performance at five different sites in Spain. Hourly forecast can achieve an adjusted R2 ∼0.99 and MAPE lower to 2.7%. SMLR yields high prediction accuracy (MAPE: 3%–10%) in four sites, but suffers issues of missing data (5%–17%) and has relatively more extreme predictions (653 observations; ±100% away from the values). MLP has less amount of extreme predictions (517 observations) with a similar accuracy (MAPE: 3%–11%), but still suffers issue of missing data (11%–28%). LSTMs also achieves good prediction accuracy (MAPE: 3%–13%) and is able to manage most extreme values. Other methods are generally less promising but can be site-specific. Understanding their distinctive characteristics can help decision-makers to rule better decisions. Exploration of future forecasts based on LSTMs shows promising (adjusted R2: 0.90–0.99; MAPE: 11%–32%) in near future (<7 h), while model optimization can be used to further improve the performance, especially for a longer gap. [Display omitted] • Hourly natural gas demands were predicted for five different sites in Spain. • Classical statistical methods were compared with deep learning approaches. • SMLR yields the highest accuracy but simulates many extreme values. • MLP and LSTMs achieve high accuracy and also produce less extreme values. • Near-future predictions are also promising for less than 7 h ahead. [ABSTRACT FROM AUTHOR]

Better municipal solid waste (MSW) management can help to address environmental concerns and supports economic and social development. Because MSW characteristics can change over time, management strategies should also evolve and be applied correspondingly. However, many previous studies have focused on MSW characterization or investigated specific management strategies for a target MSW. Few studies have assessed the spatial variations of MSW characteristics and socio-economic (SE) conditions as well as their associations. This study evaluated the feasibility of using an integrated unsupervised method (cluster analysis and cross-tabulation analysis) to explore these topics for MSW management. Results suggest that the integrated method can successfully help to reveal key information. Seven jointed MSW-SE scenarios were investigated based on 259 individual observations of Taiwan. Associations between MSW compositions and SE conditions were identified statistically significant for four MSW-SE scenarios. In general, the general SE type (SE 1) is very likely to generate high food wastes and other combustible, low paper, wood, and rubber wastes (MSW 1). The small island SE type (SE 3) is more likely to produce high paper and low wood, rubber, textile, and other noncombustible wastes (MSW 2). Overall, the method applied in this study could help to reveal statistical associations between MSW and SE, which can help decision-makers comprehend underlying facts and develop effective management strategies. • Underlying associations between MSW characteristics and SE conditions were revealed using an integrated unsupervised method. • The general SE type is likely to generate high food wastes and other combustible, low paper, wood, and rubber wastes. • The small island SE type is likely to produce high paper, low wood, rubber, textile, and other noncombustible wastes. • The method can help decision-makers comprehend underlying facts and develop effective management strategies. [ABSTRACT FROM AUTHOR]

Soft-sensor applications for wastewater management can provide valuable information for intelligent monitoring and process control above and beyond what is available from conventional hard sensors and laboratory measurements. To realize these benefits, it is important to know how to manage gaps in the data time series, which could result from the failure of hard sensors, errors in laboratory measurements, or low-frequency monitoring schedules. A robust soft-sensor system needs to include a plan to address missing data and efficiently select variable(s) to make the most use of the available information. In this study, we developed and applied an enhanced iterated stepwise multiple linear regression (ISMLR) method through a MATLAB-based package to predict the next day's influent flowrate at the Kirie water reclamation plant (WRP). The method increased the data retention from 77% to 93% and achieved an adjusted R 2 up to 0.83 by integrating with a typical artificial neural network.

Aeration accounts for a large fraction of energy consumption at conventional water reclamation plants (WRPs). Older plants were designed when control techniques were relatively primitive and energy consumption was less of a concern. As a result, although process operations at older WRPs can satisfy effluent permit requirements, they can operate with excess aeration. In this study, we developed a wastewater process model to evaluate possible aeration savings at the Metropolitan Water Reclamation District of Greater Chicago Calumet WRP, one of the oldest plants in Chicago. Based on subsets of influent characteristics, we identified eight steady-state scenarios. We also identified transient scenarios that included high probability perturbations and more challenging but lower probability conditions. Results indicate that the Calumet WRP frequently operates with excess aeration. Effluent dissolved oxygen is the limiting parameter with respect to aeration saving and permit requirements. In a typical storm event, aeration could be reduced by up to 50%; even under low probability challenging perturbations, aeration can be decreased by 35% from current average levels and all permit requirements can be satisfied. Annual cost savings from cutting the aeration by 35% could be more than $1.2 million.

A water reclamation plant (WRP) needs to be resilient to successfully operate through different kinds of perturbations. Perturbations such as storm events, especially long-term successive storm flows, can adversely affect operations. A better understanding of these effects can provide benefits for plant operation, in terms of effluent quality and energy efficiency. However, the concept of resilience for a WRP has not been widely studied, and we are not aware of any studies specifically related to storm flows. In this work we applied measures of resistance and recovery time to quantify resilience, and used a WRP simulation model to investigate how different storm flow characteristics (flowrate and duration) and the amount of aeration influence resilience. Not surprisingly, increasing storm flowrate leads to decreasing resilience. Although the aeration rate plays an important role in determining resilience, there is an aeration threshold (6 m 3 /s for our WRP model); higher aeration rates do not increase resilience. Results suggest that aeration costs could be reduced by as much as 50% while still maintaining the resilience needed to meet effluent quality permit requirements through the perturbations examined in this study., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

In this paper, we describe a biosolids mineralization process that could address some concerns about biosolids management. Solids retention in a combined anaerobic/aerobic reactor system promotes biosolids mineralization. Solids retained in the reactor were subjected to both anaerobic and aerobic degradation in two different zones in the reactor. After 267 d of operation, 75% of the solids that entered the reactors were mineralized, 62% of the total nitrogen (TN) was transformed, 51% of the phosphorus in the reactors was precipitated, and 39% of the solids dissolved and appeared in the effluent. Accumulation of solids in the reactors did not have an adverse effect on reactor performance. Evidence of biosolids degradation included a decrease in the VSS/TSS ratio, an increase in temperature, loss of nitrogen, decrease in COD, and an increase in TDS.

The sorption species and coordination environment of zinc sorbed on to hydrous ferric oxide (HFO) did not change for aging times up to six months. At an initial concentration of 10(4-) M, Zn formed innersphere surface complexes on the surface of HFO. Zn was tetrahedrally coordinated with oxygen atoms at ZnO bond distance of approximately 1.94-1.97 A with coordination number of approximately 3.8-4.7. In the second shell Zn appeared to be coordinated with Fe with a bond distance of approximately 3.42-3.49 A. At an initial concentration of 10(3-) M, both innersphere and polynuclear complexes were feasible sorption products. The first shell was tetrahedrally coordinated with about four oxygen atoms at a bond distance of 1.96 A. The second shell could be attributed to either ZnFe or ZnZn correlations with almost the same bond distance of 3.42-3.44 A.

The stress-energy tensor for the massless spin 1/2 field is numerically computed outside and on the event horizons of both charged and uncharged static nonrotating black holes, corresponding to the Schwarzschild, Reissner-Nordström, and extreme Reissner-Nordström solutions of Einstein's equations. The field is assumed to be in a thermal state at the black hole temperature. Comparison is made between the numerical results and previous analytic approximations for the stress-energy tensor in these spacetimes. For the Schwarzschild (charge zero) solution, it is shown that the stress energy differs even in sign from the analytic approximation. For the Reissner-Nordström and extreme Reissner-Nordström solutions, divergences predicted by the analytic approximations are shown not to exist.