Abimbola Motunrayo Enitan

Food processing optimization using evolutionary algorithms

Evolutionary algorithms are widely used in single and multi-objective optimization. They are easy to use and provide solution(s) in one simulation run. They are used in food processing industries for decision making. Food processing presents constrained and unconstrained optimization problems. This paper reviews the development of evolutionary algorithm techniques as used in the food processing industries. Some evolutionary algorithms like genetic algorithm, differential evolution, artificial neural networks and fuzzy logic were studied with reference to their applications in food processing. Several processes involved in food processing which include thermal processing, food quality, process design, drying, fermentation and hydrogenation processes are discussed with reference to evolutionary optimization techniques. We compared the performances of different types of evolutionary algorithm techniques and suggested further areas of application of the techniques in food processing optimization. Key words : Evolutionary algorithms, optimization, food processing, multi-objective, constrained and unconstrained.

Influence of growth conditions and nutritional requirements on the production of hydrogen peroxide by lactic acid bacteria

. The study revealed that lactic acid bacteria isolated from raw and fermented milk in South-West Nigeria are capable of producing hydrogen peroxide which has antagonistic effect on pathogenic organisms, thus, may be promising sources of preservative that may in future be applied to food. Key words:

Comparison of droplet digital PCR and quantitative PCR for the detection of Salmonella and its application for river sediments

Despite advances in microbial detection that quantitative polymerase chain reaction (qPCR) has led to, complex environmental samples, such as sediments, remain a challenge due to presence of PCR inhibitors. Aquatic sediments accumulate particle-bound microbial contaminants and thereby reflect a cumulative microbial load over time. The relatively new droplet digital PCR (ddPCR) has emerged as a direct quantitative method, highly tolerant to PCR inhibitors and relinquishing the necessity for calibration/standard curves. Information is virtually absent where ddPCR has been applied to detect pathogenic organisms in aquatic sediments. This study compared the efficacy of ddPCR with qPCR, for quantification of Salmonella in sediments from the Palmiet River near an informal settlement in Durban, South Africa. ddPCR significantly improved both analytical sensitivity and detection of low concentrations of Salmonella as compared to qPCR. The expected copy numbers measured from both qPCR and ddPCR showed good R2 values (0.999 and 0.994, respectively). The site mostly affected by the informal settlements exhibited Salmonella in the range of 255 ± 37 and 818 ± 30 Salmonella/g (p ≤ 0.0001) in qPCR and ddPCR, respectively. The improved detection of Salmonella in sediments with ddPCR makes it a promising technical method for the quantification of Salmonella in multifarious environmental samples.

Optimization of biogas generation using anaerobic digestion models and computational intelligence approaches

Abstract Anaerobic digestion (AD) technology has become popular and is widely used due to its ability to produce renewable energy from wastes. The bioenergy produced in anaerobic digesters could be directly used as fuel, thereby reducing the release of biogas to the atmosphere. Due to the limited knowledge on the different process disturbances and microbial composition that are vital for the efficient operation of AD systems, models and control strategies with respect to external influences are needed without wasting time and resources. Different simple and complex mechanistic and data-driven modeling approaches have been developed to describe the processes taking place in the AD system. Microbial activities have been incorporated in some of these models to serve as a predictive tool in biological processes. The flexibility and power of computational intelligence of evolutionary algorithms (EAs) as direct search algorithms to solve multiobjective problems and generate Pareto-optimal solutions have also been exploited. Thus, this paper reviews state-of-the-art models based on the computational optimization methods for renewable and sustainable energy optimization. This paper discusses the different types of model approaches to enhance AD processes for bioenergy generation. The optimization and control strategies using EAs for advanced reactor performance and biogas production are highlighted. This information would be of interest to a dynamic group of researchers, including microbiologists and process engineers, thereby offering the latest research advances and importance of AD technology in the production of renewable energy.

Multi-objective Optimization of Methane Producing UASB Reactor Using a Combined Pareto Multi–objective Differential Evolution Algorithm (CPMDE)

Multi–objective optimization of an operating industrial wastewater treatment plant was carried out using combined Pareto multi–objective differential evolution (CPMDE) algorithm. The algorithm combines methods of Pareto ranking and Pareto dominance selections to implement a novel selection scheme at each generation. Modified methane generation and the Stover–Kincannon kinetic mathematical models were formulated for optimization. The conflicting objective functions that are optimized in this study include, maximization of volumetric methane production rate in the biogas produced at a lower hydraulic retention time and optimum temperature; minimization of effluent substrate concentration in order to meet the environmental discharge requirements based on the standard discharge limit, and finally, the minimization of biomass washout from the reactor. Wastewater flow rate, hydraulic retention time, efficiency of substrate utilization within the reactor, influent substrate concentration and operational temperature are the important decision variables related to this process. A set of non-dominated solutions with the high methane production rate at lower biomass and almost constant solution for the effluent concentration was obtained for the multi-objective optimization problem. In this study, the simulation results showed that the CPMDE approach can generate a better Pareto-front of the selected problem and its ability to solve unconstrained, constrained and real-world optimization problem was also demonstrated.

Human Health Risk Assessment of Trace Metals in Surface Water Due to Leachate from the Municipal Dumpsite by Pollution Index: A Case Study from Ndawuse River, Abuja, Nigeria

Abstract The study assessed the level of heavy metals in surface water across Ndawuse River near the dumpsite at Phase 1 District of the Federal Capital Territory (FCT), Abuja, Nigeria. The results indicated that oxygen demand, turbidity and heavy metals were above the standard limits set for drinking water. Multivariate analysis using principal component analysis and hierarchical cluster analysis revealed natural and anthropogenic activities as sources of heavy metal contamination. The estimated non-carcinogenic effects using hazard quotient toxicity potential, cumulative hazard index and daily human exposure dose of surface water through ingestion pathway were less than a unity. The estimated carcinogenic risks (CRing) exceeded the suggested potential risk limits, with lead (Pb) having the highest CRing value for all age groups. However, children were found to be more susceptible to heavy metals over a period of time according to the estimated values. The concentration of heavy metals in the investigated river could pose an adverse health risk to several communities that rely on this receiving water bodies for domestic purposes. Therefore, there is need for strict enforcement of environmental laws to protect aquatic ecosystem and to avoid long term cumulative exposure risk that heavy metals may pose on human health.