This study investigated the application of the crystallization process for oilfield produced water from the East Baghdad oilfield affiliated to the Midland Oil Company (Iraq). Zero liquid discharge system (ZLD) consists of several parts such as oil skimming, coagulation/flocculation, forward osmosis, and crystallization, the crystallization process is a final part of a zero liquid discharge system. The laboratory-scale simple evaporation system was used to evaluate the performance of the crystallization process. In this work, sodium chloride solution and East Baghdad oilfield produced water were used as a feed solution with a concentration of 177 and 220 g/l. The impact of temperature (70, 80, and 90 °C), mixing speed (300, 400, and 500 rp

This study investigated the application of the crystallization process for oilfield produced water from the East Baghdad oilfield affiliated to the Midland Oil Company (Iraq). Zero liquid discharge system (ZLD) consists of several parts such as oil skimming, coagulation/flocculation, forward osmosis, and crystallization, the crystallization process is a final part of a zero liquid discharge system. The laboratory-scale simple evaporation system was used to evaluate the performance of the crystallization process. In this work, sodium chloride solution and East Baghdad oilfield produced water were used as a feed solution with a concentration of 177 and 220 g/l. The impact of temperature (70, 80, and 90 °C), mixing speed (300, 400, and 500

Accurate prediction and optimization of morphological traits in Roselle are essential for enhancing crop productivity and adaptability to diverse environments. In the present study, a machine learning framework was developed using Random Forest and Multi-layer Perceptron algorithms to model and predict key morphological traits, branch number, growth period, boll number, and seed number per plant, based on genotype and planting date. The dataset was generated from a field experiment involving ten Roselle genotypes and five planting dates. Both RF and MLP exhibited robust predictive capabilities; however, RF (R² = 0.84) demonstrated superior performance compared to MLP (R² = 0.80), underscoring its efficacy in capturing the nonlinear genoty

Seeds of the two rice genotypes namely Amber 33 (A33) and Amber Baghdad (AB) were divided into two groups; the first was presoaked in different concentrations of ethyl methane sulphonate (EMS) as chemical mutagen for different duration times (3, 6 and 12) hrs, and the other was exposed to different exposure times of ultra violate (UV-B) radiation (280-320 nm) as physical mutagen for different times (20, 40 and 60) min at room temperature. Treated and non-treated seeds were transferred into the callus induction medium containing 2.5 mg/L 2,4- dichlorophenoxy acetic acid (2,4-D) and 0.5 mg/L benzyl adenine (BA) under aseptic conditions. Calli were divided into two groups the first was treated with several EMS concentrations (0.0, 0.50. 1.0, 1

The result revealed that the peak of population density of cabbage aphid Brevicoryne brassicae was 523.20 individuals/plant on 21 March in edges of rapeseed field and was 1141.67 individuals/plant in center of the field. Results revealed that population density of cabbage aphid in rapeseed fields surrounded by cover crops significantly were low compared with that of monoculture rapeseed. The location of rapeseed plants (in edges or in center) significantly affected (p<0.05) the tested pest density, e.g. optimum density was 146.69 individuals/plant in the center of the field. Whereas was 93.32 in the edges. Effect of the interaction between location and surrounding vegetation was significant on aphid density, which their population densit

The need to create the optimal water quality management process has motivated researchers to pursue prediction modeling development. One of the widely important forecasting models is the sessional autoregressive integrated moving average (SARIMA) model. In the present study, a SARIMA model was developed in R software to fit a time series data of monthly fluoride content collected from six stations on Tigris River for the period from 2004 to 2014. The adequate SARIMA model that has the least Akaike's information criterion (AIC) and mean squared error (MSE) was found to be SARIMA (2, 0, 0) (0,1,1). The model parameters were identified and diagnosed to derive the forecasting equations at each selected location. The correlat

The need to create the optimal water quality management process has motivated researchers to pursue prediction modeling development. One of the widely important forecasting models is the sessional autoregressive integrated moving average (SARIMA) model. In the present study, a SARIMA model was developed in R software to fit a time series data of monthly fluoride content collected from six stations on Tigris River for the period from 2004 to 2014. The adequate SARIMA model that has the least Akaike's information criterion (AIC) and mean squared error (MSE) was found to be SARIMA (2,0,0) (0,1,1). The model parameters were identified and diagnosed to derive the forecasting equations at each selected location. The correlation coefficien