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1 |
Technical-knowledge-integrated material flow cost accounting model for energy reduction in industrial wastewater treatment
مدل حسابداری مواد مخدر فنی دانش فنی برای کاهش انرژی در درمان فاضلاب صنعتی-2021 A novel simulation model incorporating the concept of material flow cost accounting (MFCA) into a numerical
process simulator for wastewater treatment plants (WWTPs) was developed. Cost-related parameters, such as
electrical power consumption, were calculated for each unit process by referring to predetermined formulas of
design rules and technical knowledge built into the model. These calculated values were then assigned to the
outflow stream proportional to the flowrate, allowing each flow stream in the WWTP to be quantified according to
the history of assigned costs. This method increased the number of quantity centers in MFCA models regardless of
actual data availability, thus contributing complex flow configuration and flexible comparison of improvement
approaches related to financial evaluation. Energy cost allocation maps created by this model demonstrated the
benefits of anaerobic treatment in the WWTP of a soft-drink factory in Japan. Additionally in this WWTP, the
observed values of total power consumption were 40% higher than the simulated values, and improvement approaches, such as instrumental control of aeration, were evaluated for their feasibility and financial impact. These
results demonstrated the success of the model in adding and reinforcing analytical and predictive functions in the
MFCA survey method.
Keywords: Material flow cost accounting | Process simulation model | Industrial wastewater | Energy saving | Food and beverage industry |
مقاله انگلیسی |
2 |
Assessment of weather-based influent scenarios for a WWTP: Application of a pattern recognition technique
ارزیابی سناریوهای تأثیرگذار بر اساس آب و هوا برای WWTP: استفاده از یک روش تشخیص الگو-2019 This study proposes an integrated approach by combining a pattern recognition technique and a process simulation
model, to assess the impact of various climatic conditions on influent characteristics of the largest
Italian wastewater treatment plant (WWTP) at Castiglione Torinese. Eight years (viz. 2009–2016) of historical
influent data namely influent flow rate (Qin), chemical oxygen demand (COD), ammonium (N-NH4) and total
suspended solids (TSS), in addition to two climatic attributes, average temperature and daily mean precipitation
rates (PI) from the plant catchment area, are evaluated in this study. Following the outlier removal and missingdata
imputation, five influent climate-based scenarios are identified by K-means clustering approach. Statistical
characteristics of clustered observations are further investigated. Finally, to demonstrate that the proposed
approach could improve the process control and efficiency, a process simulation model was developed and
calibrated. Steady-state simulations were conducted, and the performance of the plant was studied under five
influent scenarios. Further, an optimization scenario-based method was conducted to improve the energy consumption
of the plant while meeting effluent requirements. The results indicate that with the adaptation of
suitable aeration strategies for each of the influent scenarios, 10–40% energy saving can be achieved while
meeting effluent requirements. Keywords: Wastewater treatment plant (WWTP) | Influent data | K-means clustering | Climatic data | Python |
مقاله انگلیسی |