Abstract: Anaerobic digestion process is one of the alternative
methods to convert organic waste into methane gas which is a fuel
and energy source. Activities of various kinds of microorganisms are
the main factor for anaerobic digestion which produces methane gas.
Therefore, in this study a modified Anaerobic Baffled Reactor (ABR)
with working volume of 50 liters was designed to identify the
microorganisms through biogas production. The mixture of 75%
kitchen waste and 25% sewage sludge was used as substrate.
Observations on microorganisms in the ABR showed that there exists
a small amount of protozoa (5%) and fungi (2%) in the system, but
almost 93% of the microorganism population consists of bacteria. It
is definitely clear that bacteria are responsible for anaerobic
biodegradation of kitchen waste. Results show that in the
acidification zone of the ABR (front compartments of reactor) fast
growing bacteria capable of growth at high substrate levels and
reduced pH was dominant. A shift to slower growing scavenging
bacteria that grow better at higher pH was occurring towards the end
of the reactor. Due to the ability of activity in acetate environment the
percentages of Methanococcus, Methanosarcina and Methanotrix
were higher than other kinds of methane former in the system.
Abstract: An experimental study of anaerobic treatment was performed by hybrid upflow anaerobic sludge blanket (HUASB) reactor to treat produced water (PW) of an onshore crude oil terminal (COD: 1597 mg/L, NH3-N: 14.7 mg/L, phenol: 13.8 mg/L, BOD5: 862 mg/L, sodium: 6240 mg/L and chloride 9530 mg/L). The produced water with high salinity and other toxic substances will inhibit the methanogens performance if there is no adaptation on biomass before anaerobic digestion. COD removal from produced water was investigated at five different dilutions of produced water and tap water (TW) without any nutrient addition and pre-treatment. The dilution ratios were 1PW:4TW, 2PW:3TW, 3PW:2TW, 4PW:1TW and 5PW:0TW. The reactor was evaluated at mesophilic operating condition (35 ± 2 °C) at 5 days of HRT for 250 days continuous feed. The average COD removals for 1PW:4TW, 2PW:3TW, 3PW:2TW, 4PW:1TW and 5PW:0TW were found to be approximately 76.1%, 73.8%, 70.3%, 46.3% and 61.82% respectively, with final average effluent COD of 123.7 mg/L, 240 mg/L, 294 mg/L, 589 mg/L and 738 mg/L, respectively.
Abstract: Many non-conventional adsorbent have been studied
as economic alternative to commercial activated carbon and mostly
agricultural waste have been introduced such as rubber leaf powder
and hazelnut shell. Microwave Incinerated Rice Husk Ash
(MIRHA), produced from the rice husk is one of the low-cost
materials that were used as adsorbent of heavy metal. The aim of
this research was to study the feasibility of using MIRHA500 and
MIRHA800 as adsorbent for the removal of Cu(II) metal ions from
aqueous solutions by the batch studies. The adsorption of Cu(II) into
MIRHA500 and MIRH800 favors Fruendlich isotherm and imply
pseudo – kinetic second order which applied chemisorptions
Abstract: Water hyacinth has been used in aquatic systems for
wastewater purification in many years worldwide. The role of water
hyacinth (Eichhornia crassipes) species in polishing nitrate and
phosphorus concentration from municipal wastewater treatment plant
effluent by phytoremediation method was evaluated. The objective
of this project is to determine the removal efficiency of water
hyacinth in polishing nitrate and phosphorus, as well as chemical
oxygen demand (COD) and ammonia. Water hyacinth is considered
as the most efficient aquatic plant used in removing vast range of
pollutants such as organic matters, nutrients and heavy metals. Water
hyacinth, also referred as macrophytes, were cultivated in the
treatment house in a reactor tank of approximately 90(L) x 40(W) x
25(H) in dimension and built with three compartments. Three water
hyacinths were placed in each compartments and water sample in
each compartment were collected in every two days. The plant
observation was conducted by weight measurement, plant uptake and
new young shoot development. Water hyacinth effectively removed
approximately 49% of COD, 81% of ammonia, 67% of phosphorus
and 92% of nitrate. It also showed significant growth rate at starting
from day 6 with 0.33 shoot/day and they kept developing up to 0.38
shoot/day at the end of day 24. From the studies conducted, it was
proved that water hyacinth is capable of polishing the effluent of
municipal wastewater which contains undesirable amount of nitrate
and phosphorus concentration.
Abstract: Cameron Highlands is a mountainous area subjected
to torrential tropical showers. It extracts 5.8 million liters of water
per day for drinking supply from its rivers at several intake points.
The water quality of rivers in Cameron Highlands, however, has
deteriorated significantly due to land clearing for agriculture,
excessive usage of pesticides and fertilizers as well as construction
activities in rapidly developing urban areas. On the other hand, these
pollution sources known as non-point pollution sources are diverse
and hard to identify and therefore they are difficult to estimate.
Hence, Geographical Information Systems (GIS) was used to provide
an extensive approach to evaluate landuse and other mapping
characteristics to explain the spatial distribution of non-point sources
of contamination in Cameron Highlands. The method to assess
pollution sources has been developed by using Cameron Highlands
Master Plan (2006-2010) for integrating GIS, databases, as well as
pollution loads in the area of study. The results show highest annual
runoff is created by forest, 3.56 × 108 m3/yr followed by urban
development, 1.46 × 108 m3/yr. Furthermore, urban development
causes highest BOD load (1.31 × 106 kgBOD/yr) while agricultural
activities and forest contribute the highest annual loads for
phosphorus (6.91 × 104 kgP/yr) and nitrogen (2.50 × 105 kgN/yr),
respectively. Therefore, best management practices (BMPs) are
suggested to be applied to reduce pollution level in the area.
Abstract: The use of artificial neural network (ANN) modeling
for prediction and forecasting variables in water resources
engineering are being increasing rapidly. Infrastructural applications
of ANN in terms of selection of inputs, architecture of networks,
training algorithms, and selection of training parameters in different
types of neural networks used in water resources engineering have
been reported. ANN modeling conducted for water resources
engineering variables (river sediment and discharge) published in
high impact journals since 2002 to 2011 have been examined and
presented in this review. ANN is a vigorous technique to develop
immense relationship between the input and output variables, and
able to extract complex behavior between the water resources
variables such as river sediment and discharge. It can produce robust
prediction results for many of the water resources engineering
problems by appropriate learning from a set of examples. It is
important to have a good understanding of the input and output
variables from a statistical analysis of the data before network
modeling, which can facilitate to design an efficient network. An
appropriate training based ANN model is able to adopt the physical
understanding between the variables and may generate more effective
results than conventional prediction techniques.