Development of a Real-Time Controlled Bio-Liquor Circulation System for Swine Farms: A Lab-Scale Study

Response surface methodology for process optimization in livestock wastewater treatment: A review

With increasing demand for meat and dairy products, the volume of wastewater generated from the livestock industry has become a significant environmental concern. The treatment of livestock wastewater (LWW) is a challenging process that involves removing nutrients, organic matter, pathogens, and other pollutants from livestock manure and urine. In response to this challenge, researchers have developed and investigated different biological, physical, and chemical treatment technologies that perform better upon optimization. Optimization of LWW handling processes can help improve the efficacy and sustainability of treatment systems as well as minimize environmental impacts and associated costs. Response surface methodology (RSM) as an optimization approach can effectively optimize operational parameters that affect process performance. This review article summarizes the main steps of RSM, recent applications of RSM in LWW treatment, highlights the advantages and limitations of this technique, and provides recommendations for future research and practice, including its cost-effectiveness, accuracy, and ability to improve treatment efficiency.

Assessment of a Novel Real-Time Bio-Liquor Circulation System for Manure Management and Mitigation of Odor Potential in Swine Farming

Recently, circulating biologically treated manure in slurry pits has been used as an odor reduction technology, but few successful results have been reported, due to the lack of proper control strategies for bioreactors. This study was conducted to investigate the performance of the developed real-time controlled bio-liquor circulation system (BCS) at farm scale. The BCS was operated sequentially as per swine manure inflow (anoxic, aerobic, and settling) circulation to the slurry pit. Each operational phase was self-adjusted in real-time using a novel algorithm for detecting the control point on the oxidation reduction potential (ORP) and pH (mV)-time profiles, the nitrogen break point (NBP), and the nitrate knee point (NKP) in the aerobic and anoxic phases, respectively. The NH4-N in the slurry manure was thoroughly removed (100%) in the bioreactor, optimizing the duration of each operational phase by accurately detecting real-time control points. The newly developed real-time BCS decreased the nitrogen and organic matter in the slurry pit by >70%, and the potential ammonia and methane emissions by 75% and 95%, respectively. This study highlights that improved BCS that utilizes ORP tracking and pH (mV)-time profiles can effectively optimize BCS operation, and thereby reduce malodor and GHG emissions from swine farms.

Real-Time Control Technology for a Bio-Liquor Circulation System in a Swine Barn with Slurry Pit: Pilot Scale Study

The livestock industry, especially swine production, has been pressurized by vicinity complaints about odor in Korea. Therefore, a lot of effort has been undertaken regarding reducing the odor emissions from pigsties, widely carried out and the washing out manure in slurry pit by liquid-phase compost has particularly been spotlighted with outstanding performance of odor reduction. However, such a washing out manure called bio-liquor circulation system (BCS) has been controlled by a timer with designated reaction time, which cannot guarantee the system performance. This research proposes an effective real-time control technology for BCS, which circulates bio-liquor to the slurry pit of swine barns. The real-time control system was operated through accurate detection of the designated control points on the oxidation reduction potential (ORP) and pH time profiles for the nitrate knee point (NKP) and nitrogen break point (NBP) in anoxic and aerobic conditions with 100 and 99.6% performances, respectively. The duration of the anoxic and aerobic phases was also automated and noticeably lowered the concentration of nutrients in the manure in the slurry-pit, which served as a source of malodor. The real-time control strategy may be an innovative way to reduce odor and simultaneously produce liquid fertilizer, and provides a reference for the optimization of the industrial scale.

Measuring the Economic Value of the Negative Externality of Livestock Malodor in South Korea

The South Korean livestock industry has increased in scale and production, generating positive impacts on the national economy. However, livestock externalities, primarily malodor, have subsequently led to increased conflicts between producers and affected communities. This study estimated Korean households’ willingness-to-pay (WTP) for government subsidies to help address livestock malodor using a contingent valuation method (CVM) derived from a double-bounded dichotomous choice model. The annual average household WTP was estimated at 29,206 Korean won (KRW) (USD 25). This was slightly higher than the respondents’ self-reported average amount of KRW 25,457 (USD 22). The estimated economic value nationally is KRW 628 billion (USD 546 million) annually, for a total of KRW 3.14 trillion (USD 2.73 billion) over a proposed five-year period. The public’s estimated WTP can be leveraged to improve livestock management practices, more efficient waste disposal techniques, and improved husbandry methods to address conflicts between producers and surrounding communities.

Optimization and Modeling of Ammonia Nitrogen Removal from High Strength Synthetic Wastewater Using Vacuum Thermal Stripping

Waste streams with high ammonia nitrogen (NH3-N) concentrations are very commonly produced due to human intervention and often end up in waterbodies with effluent discharge. The removal of NH3-N from wastewater is therefore of utmost importance to alleviate water quality issues including eutrophication and fouling. In the present study, vacuum thermal stripping of NH3-N from high strength synthetic wastewater was conducted using a rotary evaporator and the process was optimized and modeled using response surface methodology (RSM) and RSM–artificial neural network (ANN) approaches. RSM was first employed to evaluate the process performance using three independent variables, namely pH, temperature (°C) and stripping time (min), and the optimal conditions for NH3-N removal (response) were determined. Later, the obtained data from the designed experiments of RSM were used to train the ANN for predicting the responses. NH3-N removal was found to be 97.84 ± 1.86% under the optimal conditions (pH: 9.6, temperature: 65.5 °C, and stripping time: 59.6 min) and was in good agreement with the values predicted by RSM and RSM–ANN models. A statistical comparison between the models revealed the better predictability of RSM–ANN than that of the RSM. To the best of our knowledge, this is the first attempt comparing the RSM and RSM–ANN in vacuum thermal stripping of NH3-N from wastewater. The findings of this study can therefore be useful in designing and carrying out the vacuum thermal stripping process for efficient removal of NH3-N from wastewater under different operating conditions.