Research article

Statistical property analysis for a stochastic chemostat model with degenerate diffusion

  • By considering the fact that the growth of microorganisms in a chemostat is subject to white noise, we construct a stochastic chemostat model with degenerate diffusion by using a discrete Markov chain. By solving the corresponding Fokker-Planck equation, we derive the explicit expression of the stationary joint probability density, which peaks near the deterministic equilibrium. Next, we simulate the the marginal probability density functions for different noise intensities and further discuss the relationship of the marginal probability density function and noise intensities. For the statistical properties of the stochastic model, we mainly investigate the effect of white noise on the variance and skewness of the concentration of microorganisms.

    Citation: Jingen Yang, Zhong Zhao, Xinyu Song. Statistical property analysis for a stochastic chemostat model with degenerate diffusion[J]. AIMS Mathematics, 2023, 8(1): 1757-1769. doi: 10.3934/math.2023090

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  • By considering the fact that the growth of microorganisms in a chemostat is subject to white noise, we construct a stochastic chemostat model with degenerate diffusion by using a discrete Markov chain. By solving the corresponding Fokker-Planck equation, we derive the explicit expression of the stationary joint probability density, which peaks near the deterministic equilibrium. Next, we simulate the the marginal probability density functions for different noise intensities and further discuss the relationship of the marginal probability density function and noise intensities. For the statistical properties of the stochastic model, we mainly investigate the effect of white noise on the variance and skewness of the concentration of microorganisms.



    In this article, we study the oscillatory behavior of the fourth-order neutral nonlinear differential equation of the form

    {(r(t)Φp1[w(t)])+q(t)Φp2(u(ϑ(t)))=0,r(t)>0, r(t)0, tt0>0, (1.1)

    where w(t):=u(t)+a(t)u(τ(t)) and the first term means the p-Laplace type operator (1<p<). The main results are obtained under the following conditions:

    L1: Φpi[s]=|s|pi2s, i=1,2,

    L2: rC[t0,) and under the condition

    t01r1/(p11)(s)ds=. (1.2)

    L3: a,qC[t0,), q(t)>0, 0a(t)<a0<, τ,ϑC[t0,), τ(t)t, limtτ(t)=limtϑ(t)=

    By a solution of (1.1) we mean a function u C3[tu,), tut0, which has the property r(t)(w(t))p11C1[tu,), and satisfies (1.1) on [tu,). We assume that (1.1) possesses such a solution. A solution of (1.1) is called oscillatory if it has arbitrarily large zeros on [tu,), and otherwise it is called to be nonoscillatory. (1.1) is said to be oscillatory if all its solutions are oscillatory.

    We point out that delay differential equations have applications in dynamical systems, optimization, and in the mathematical modeling of engineering problems, such as electrical power systems, control systems, networks, materials, see [1]. The p-Laplace equations have some significant applications in elasticity theory and continuum mechanics.

    During the past few years, there has been constant interest to study the asymptotic properties for oscillation of differential equations with p-Laplacian like operator in the canonical case and the noncanonical case, see [2,3,4,11] and the numerical solution of the neutral delay differential equations, see [5,6,7]. The oscillatory properties of differential equations are fairly well studied by authors in [16,17,18,19,20,21,22,23,24,25,26,27]. We collect some relevant facts and auxiliary results from the existing literature.

    Liu et al. [4] studied the oscillation of even-order half-linear functional differential equations with damping of the form

    {(r(t)Φ(y(n1)(t)))+a(t)Φ(y(n1)(t))+q(t)Φ(y(g(t)))=0,Φ=|s|p2s, tt0>0,

    where n is even. This time, the authors used comparison method with second order equations.

    The authors in [9,10] have established sufficient conditions for the oscillation of the solutions of

    {(r(t)|y(n1)(t)|p2y(n1)(t))+ji=1qi(t)g(y(ϑi(t)))=0,j1, tt0>0,

    where n is even and p>1 is a real number, in the case where ϑi(t)υ (with rC1((0,),R), qiC([0,),R), i=1,2,..,j).

    We point out that Li et al. [3] using the Riccati transformation together with integral averaging technique, focuses on the oscillation of equation

    {(r(t)|w(t)|p2w(t))+ji=1qi(t)|y(δi(t))|p2y(δi(t))=0,1<p<, , tt0>0.

    Park et al. [8] have obtained sufficient conditions for oscillation of solutions of

    {(r(t)|y(n1)(t)|p2y(n1)(t))+q(t)g(y(δ(t)))=0,1<p<, , tt0>0.

    As we already mentioned in the Introduction, our aim here is complement results in [8,9,10]. For this purpose we discussed briefly these results.

    In this paper, we obtain some new oscillation criteria for (1.1). The paper is organized as follows. In the next sections, we will mention some auxiliary lemmas, also, we will use the generalized Riccati transformation technique to give some sufficient conditions for the oscillation of (1.1), and we will give some examples to illustrate the main results.

    For convenience, we denote

    A(t)=q(t)(1a0)p21Mp1p2(ϑ(t)), B(t)=(p11)εϑ2(t)ζϑ(t)r1/(p11)(t), ϕ1(t)=tA(s)ds,R1(t):=(p11)μt22r1/(p11)(t),ξ(t):=q(t)(1a0)p21Mp2p11ε1(ϑ(t)t)3(p21),η(t):=(1a0)p2/p1Mp2/(p12)2t(1r(δ)δq(s)ϑp21(s)sp21ds)1/(p11)dδ,ξ(t)=tξ(s)ds, η(t)=tη(s)ds,

    for some μ(0,1) and every M1,M2 are positive constants.

    Definition 1. A sequence of functions {δn(t)}n=0 and {σn(t)}n=0 as

    δ0(t)=ξ(t), and σ0(t)=η(t),δn(t)=δ0(t)+tR1(t)δp1/(p11)n1(s)ds, n>1σn(t)=σ0(t)+tσp1/(p11)n1(s)ds, n>1. (2.1)

    We see by induction that δn(t)δn+1(t) and σn(t)σn+1(t) for tt0, n>1.

    In order to discuss our main results, we need the following lemmas:

    Lemma 2.1. [12] If the function w satisfies w(i)(ν)>0, i=0,1,...,n, and w(n+1)(ν)<0  eventually. Then, for every ε1(0,1), w(ν)/w(ν)ε1ν/n eventually.

    Lemma 2.2. [13] Let u(t) be a positive and n-times differentiable function on an interval [T,) with its nth derivative u(n)(t) non-positive on [T,) and not identically zero on any interval of the form [T,), TT and u(n1)(t)u(n)(t)0, ttu then there exist constants θ, 0<θ<1  and ε>0 such that

    u(θt)εtn2u(n1)(t),

    for all sufficient large t.

    Lemma 2.3 [14] Let uCn([t0,),(0,)). Assume that u(n)(t) is of fixed sign and not identically zero on [t0,) and that there exists a t1t0 such that u(n1)(t)u(n)(t)0 for all tt1. If limtu(t)0, then for every μ(0,1) there exists tμt1 such that

    u(t)μ(n1)!tn1|u(n1)(t)| for ttμ.

    Lemma 2.4. [15] Assume that (1.2) holds and u is an eventually positive solution of (1.1). Then, (r(t)(w(t))p11)<0 and there are the following two possible cases eventually:

    (G1) w(k)(t)>0, k=1,2,3,(G2) w(k)(t)>0, k=1,3, and w(t)<0.

    Theorem 2.1. Assume that

    liminft1ϕ1(t)tB(s)ϕp1(p11)1(s)ds>p11pp1(p11)1. (2.2)

    Then (1.1) is oscillatory.

    proof. Assume that u be an eventually positive solution of (1.1). Then, there exists a t1t0 such that u(t)>0, u(τ(t))>0 and u(ϑ(t))>0 for tt1. Since r(t)>0, we have

    w(t)>0, w(t)>0, w(t)>0, w(4)(t)<0 and (r(t)(w(t))p11)0, (2.3)

    for tt1. From definition of w, we get

    u(t)w(t)a0u(τ(t))w(t)a0w(τ(t))(1a0)w(t),

    which with (1.1) gives

    (r(t)(w(t))p11)q(t)(1a0)p21wp21(ϑ(t)). (2.4)

    Define

    ϖ(t):=r(t)(w(t))p11wp11(ζϑ(t)). (2.5)

    for some a constant ζ(0,1). By differentiating and using (2.4), we obtain

    ϖ(t)q(t)(1a0)p21wp21(ϑ(t)).wp11(ζϑ(t))(p11)r(t)(w(t))p11w(ζϑ(t))ζϑ(t)wp1(ζϑ(t)).

    From Lemma 2.2, there exist constant ε>0, we have

    ϖ(t)q(t)(1a0)p21wp2p1(ϑ(t))(p11)r(t)(w(t))p11εϑ2(t)w(ϑ(t))ζϑ(t)wp1(ζϑ(t)).

    Which is

    ϖ(t)q(t)(1a0)p21wp2p1(ϑ(t))(p11)εr(t)ϑ2(t)ζϑ(t)(w(t))p1wp1(ζϑ(t)),

    by using (2.5) we have

    ϖ(t)q(t)(1a0)p21wp2p1(ϑ(t))(p11)εϑ2(t)ζϑ(t)r1/(p11)(t)ϖp1/(p11)(t). (2.6)

    Since w(t)>0, there exist a t2t1 and a constant M>0 such that

    w(t)>M.

    Then, (2.6), turns to

    ϖ(t)q(t)(1a0)p21Mp2p1(ϑ(t))(p11)εϑ2(t)ζϑ(t)r1/(p11)(t)ϖp1/(p11)(t),

    that is

    ϖ(t)+A(t)+B(t)ϖp1/(p11)(t)0.

    Integrating the above inequality from t to l, we get

    ϖ(l)ϖ(t)+ltA(s)ds+ltB(s)ϖp1/(p11)(s)ds0.

    Letting l and using ϖ>0 and ϖ<0, we have

    ϖ(t)ϕ1(t)+tB(s)ϖp1/(p11)(s)ds.

    This implies

    ϖ(t)ϕ1(t)1+1ϕ1(t)tB(s)ϕp1/(p11)1(s)(ϖ(s)ϕ1(s))p1/(p11)ds. (2.7)

    Let λ=inftTϖ(t)/ϕ1(t) then obviously λ1. Thus, from (2.2) and (2.7) we see that

    λ1+(p11)(λp1)p1/(p11)

    or

    λp11p1+(p11)p1(λp1)p1/(p11),

    which contradicts the admissible value of λ1 and (p11)>0.

    Therefore, the proof is complete.

    Theorem 2.2. Assume that

    liminft1ξ(t)tR1(s)ξp1/(p11)(s)ds>(p11)pp1/(p11)1 (2.8)

    and

    liminft1η(t)t0η2(s)ds>14. (2.9)

    Then (1.1) is oscillatory.

    proof. Assume to the contrary that (1.1) has a nonoscillatory solution in [t0,). Without loss of generality, we let u be an eventually positive solution of (1.1). Then, there exists a t1t0 such that u(t)>0, u(τ(t))>0 and u(ϑ(t))>0 for tt1. From Lemma 2.4 there is two cases (G1) and (G2).

    For case (G1). Define

    ω(t):=r(t)(w(t))p11wp11(t).

    By differentiating ω and using (2.4), we obtain

    ω(t)q(t)(1a0)p21wp21(ϑ(t))wp11(t)(p11)r(t)(w(t))p11wp1(t)w(t). (2.10)

    From Lemma 2.1, we get

    w(t)w(t)3ε1t.

    Integrating again from t to ϑ(t), we find

    w(ϑ(t))w(t)ε1ϑ3(t)t3. (2.11)

    It follows from Lemma 2.3 that

    w(t)μ12t2w(t), (2.12)

    for all μ1(0,1) and every sufficiently large t. Since w(t)>0, there exist a t2t1 and a constant M>0 such that

    w(t)>M, (2.13)

    for tt2. Thus, by (2.10), (2.11), (2.12) and (2.13), we get

    ω(t)+q(t)(1a0)p21Mp2p11ε1(ϑ(t)t)3(p21)+(p11)μt22r1/(p11)(t)ωp1/(p11)(t)0,

    that is

    ω(t)+ξ(t)+R1(t)ωp1/(p11)(t)0. (2.14)

    Integrating (2.14) from t to l, we get

    ω(l)ω(t)+ltξ(s)ds+ltR1(s)ωp1/(p11)(s)ds0.

    Letting l and using ω>0 and ω<0, we have

    ω(t)ξ(t)+tR1(s)ωp1/(p11)(s)ds. (2.15)

    This implies

    ω(t)ξ(t)1+1ξ(t)tR1(s)ξp1/(p11)(s)(ω(s)ξ(s))p1/(p11)ds. (2.16)

    Let λ=inftTω(t)/ξ(t) then obviously λ1. Thus, from (2.8) and (2.16) we see that

    λ1+(p11)(λp1)p1/(p11)

    or

    λp11p1+(p11)p1(λp1)p1/(p11),

    which contradicts the admissible value of λ1 and (p11)>0.

    For case (G2). Integrating (2.4) from t to m, we obtain

    r(m)(w(m))p11r(t)(w(t))p11mtq(s)(1a0)p21wp21(ϑ(s))ds. (2.17)

    From Lemma 2.1, we get that

    w(t)ε1tw(t) and hence w(ϑ(t))ε1ϑ(t)tw(t). (2.18)

    For (2.17), letting mand using (2.18), we see that

    r(t)(w(t))p11ε1(1a0)p21wp21(t)tq(s)ϑp21(s)sp21ds.

    Integrating this inequality again from t to , we get

    w(t)ε1(1a0)p2/p1wp2/p1(t)t(1r(δ)δq(s)ϑp21(s)sp21ds)1/(p11)dδ, (2.19)

    for all ε1(0,1). Define

    y(t)=w(t)w(t).

    By differentiating y and using (2.13) and (2.19), we find

    y(t)=w(t)w(t)(w(t)w(t))2y2(t)(1a0)p2/p1M(p2/p1)1t(1r(δ)δq(s)ϑp21(s)sp21ds)1/(p11)dδ, (2.20)

    hence

    y(t)+η(t)+y2(t)0. (2.21)

    The proof of the case where (G2) holds is the same as that of case (G1). Therefore, the proof is complete.

    Theorem 2.3. Let δn(t) and σn(t) be defined as in (2.1). If

    limsupt(μ1t36r1/(p11)(t))p11δn(t)>1 (2.22)

    and

    limsuptλtσn(t)>1, (2.23)

    for some n, then (1.1)is oscillatory.

    proof. Assume to the contrary that (1.1) has a nonoscillatory solution in [t0,). Without loss of generality, we let u be an eventually positive solution of (1.1). Then, there exists a t1t0 such that u(t)>0, u(τ(t))>0 and u(ϑ(t))>0 for tt1. From Lemma 2.4 there is two cases.

    In the case (G1), proceeding as in the proof of Theorem 2.2, we get that (2.12) holds. It follows from Lemma 2.3 that

    w(t)μ16t3w(t). (2.24)

    From definition of ω(t) and (2.24), we have

    1ω(t)=1r(t)(w(t)w(t))p111r(t)(μ16t3)p11.

    Thus,

    ω(t)(μ1t36r1/(p11)(t))p111.

    Therefore,

    limsuptω(t)(μ1t36r1/(p11)(t))p111,

    which contradicts (2.22).

    The proof of the case where (G2) holds is the same as that of case (G1). Therefore, the proof is complete.

    Corollary 2.1. Let δn(t) and σn(t) be defined as in (2.1). If

    t0ξ(t)exp(tt0R1(s)δ1/(p11)n(s)ds)dt= (2.25)

    and

    t0η(t)exp(tt0σ1/(p11)n(s)ds)dt=, (2.26)

    for some n, then (1.1) is oscillatory.

    proof. Assume to the contrary that (1.1) has a nonoscillatory solution in [t0,). Without loss of generality, we let u be an eventually positive solution of (1.1). Then, there exists a t1t0 such that u(t)>0, u(τ(t))>0 and u(ϑ(t))>0 for tt1. From Lemma 2.4 there is two cases (G1) and (G2).

    In the case (G1), proceeding as in the proof of Theorem 2, we get that (2.15) holds. It follows from (2.15) that ω(t)δ0(t).  Moreover, by induction we can also see that ω(t)δn(t) for tt0, n>1. Since the sequence {δn(t)}n=0 monotone increasing and bounded above, it converges to δ(t). Thus, by using Lebesgue's monotone convergence theorem, we see that

    δ(t)=limnδn(t)=tR1(t)δp1/(p11)(s)ds+δ0(t)

    and

    δ(t)=R1(t)δp1/(p11)(t)ξ(t). (2.27)

    Since δn(t)δ(t), it follows from (2.27) that

    δ(t)R1(t)δ1/(p11)n(t)δ(t)ξ(t).

    Hence, we get

    δ(t)exp(tTR1(s)δ1/(p11)n(s)ds)(δ(T)tTξ(s)exp(sTR1(δ)δ1/(p11)n(δ)dδ)ds).

    This implies

    tTξ(s)exp(sTR1(δ)δ1/(p11)n(δ)dδ)dsδ(T)<,

    which contradicts (2.25). The proof of the case where (G2) holds is the same as that of case (G1). Therefore, the proof is complete.

    Example 2.1. Consider the differential equation

    (u(t)+12u(t2))(4)+q0t4u(t3)=0,  (2.28)

    where q0>0 is a constant. Let p1=p2=2, r(t)=1, a(t)=1/2, τ(t)=t/2, ϑ(t)=t/3 and q(t)=q0/t4. Hence, it is easy to see that

    A(t)=q(t)(1a0)(p21)Mp2p1(ϑ(t))=q02t4, B(t)=(p11)εϑ2(t)ζϑ(t)r1/(p11)(t)=εt227

    and

    ϕ1(t)=q06t3,

    also, for some ε>0, we find

    liminft1ϕ1(t)tB(s)ϕp1/(p11)1(s)ds>(p11)pp1/(p11)1.liminft6εq0t3972tdss4>14q0>121.5ε.

    Hence, by Theorem 2.1, every solution of Eq (2.28) is oscillatory if q0>121.5ε.

    Example 2.2. Consider a differential equation

    (u(t)+a0u(τ0t))(n)+q0tnu(ϑ0t)=0, (2.29)

    where q0>0 is a constant. Note that p=2, t0=1, r(t)=1, a(t)=a0, τ(t)=τ0t, ϑ(t)=ϑ0t  and q(t)=q0/tn.

    Easily, we see that condition (2.8) holds and condition (2.9) satisfied.

    Hence, by Theorem 2.2, every solution of Eq (2.29) is oscillatory.

    Remark 2.1. Finally, we point out that continuing this line of work, we can have oscillatory results for a fourth order equation of the type:

    {(r(t)|y(t)|p12y(t))+a(t)f(y(t))+ji=1qi(t)|y(σi(t))|p22y(σi(t))=0,tt0, σi(t)t, j1,, 1<p2p1<.

    The paper is devoted to the study of oscillation of fourth-order differential equations with p-Laplacian like operators. New oscillation criteria are established by using a Riccati transformations, and they essentially improves the related contributions to the subject.

    Further, in the future work we get some Hille and Nehari type and Philos type oscillation criteria of (1.1) under the condition υ01r1/(p11)(s)ds<.

    The authors express their debt of gratitude to the editors and the anonymous referee for accurate reading of the manuscript and beneficial comments.

    The author declares that there is no competing interest.



    [1] H. L. Smith, P. Waltman, The theory of the chemostat: Dynamics of microbial competition, Cambridge: Cambridge University Press, 1995.
    [2] P. Fergola, C. Tenneriello, Z. Ma, X. Wen, Effects of toxicants on chemostat models, Cybernet. Syst., 94 (1994), 887–894.
    [3] M. Nelson, H. Sidhu, Reducing the emission of pollutants in food processing wastewaters, Chem. Eng. Process., 46 (2007), 429–436. https://doi.org/10.1016/j.cep.2006.04.012 doi: 10.1016/j.cep.2006.04.012
    [4] D. H. Nguyen, N. Nguyen, G. Yin, General nonlinear stochastic systems motivated by chemostat models: Complete characterization of long-time behavior, optimal controls, and applications to wastewater treatment, Stoch. Proc. Appl., 130 (2017), 4608–4642. https://doi.org/10.1016/j.spa.2020.01.010 doi: 10.1016/j.spa.2020.01.010
    [5] Y. Sabbar, A. Din, D. Kiouach, Predicting potential scenarios for wastewater treatment under unstable physical and chemical laboratory conditions: A mathematical study, Results Phys., 39 (2022), 105717. https://doi.org/10.1016/j.rinp.2022.105717 doi: 10.1016/j.rinp.2022.105717
    [6] Y. Sabbar, A. Zeb, D. Kiouach, N. Gul, T. Sitthiwirattham, D. Baleanu, et al., Dynamical bifurcation of a sewage treatment model with general higher-order perturbation, Results Phys., 39 (2022), 105799. https://doi.org/10.1016/j.rinp.2022.105799 doi: 10.1016/j.rinp.2022.105799
    [7] S. B. Hsu, S. P. Hubbell, P. Waltman, A mathematical theory for single-nutrient competition in continuous cultures of micro-organisms, SIAM J. Appl. Math., 32 (1977), 366–383. https://doi.org/10.1137/0132030 doi: 10.1137/0132030
    [8] T. C. Gard, A new Liapunov function for the simple chemostat, Nonlinear Anal-Real., 3 (2002), 211–226. https://doi.org/10.1016/S1468-1218(01)00023-2 doi: 10.1016/S1468-1218(01)00023-2
    [9] Z. Zhong, L. Chen, X. Song, Extinction and permanence of chemostat model with pulsed input in a polluted environment, Commu. Nonlinear Sci., 14 (2009), 1737–1745. https://doi.org/10.1016/j.cnsns.2008.01.009 doi: 10.1016/j.cnsns.2008.01.009
    [10] J. Jiao, X. Yang, L. Chen, S. Cai, Effect of delayed response in growth on the dynamics of a chemostat model with impulsive input, Chaos Soliton. Fract., 42 (2009), 2280–2287. https://doi.org/10.1016/j.chaos.2009.03.132 doi: 10.1016/j.chaos.2009.03.132
    [11] J. Shi, Y. Wu, X. Zou, Coexistence of competing species for intermediate dispersal rates in a reaction Cdiffusion chemostat model, J. Dyn. Differ. Equ., 32 (2020), 1085–1112. https://doi.org/10.1007/s10884-019-09763-0 doi: 10.1007/s10884-019-09763-0
    [12] E. O. Alzahrani, M. M. El-Dessoky, P. Dogra, Global dynamics of a cell quota-based model of light-dependent algae growth in a chemostat, Commu. Nonlinear Sci., 90 (2020), 105295. https://doi.org/10.1016/j.cnsns.2020.105295 doi: 10.1016/j.cnsns.2020.105295
    [13] R. Baratti, J. Alvarez, S. Tronci, M. Grosso, A. Schaum, Characterization with Fokker-Planck theory of the nonlinear stochastic dynamics of a class of two-state continuous bioreactors, J. Process Contr., 102 (2021), 66–84. https://doi.org/10.1016/j.jprocont.2021.04.004 doi: 10.1016/j.jprocont.2021.04.004
    [14] Y. Lu, Z. Fang, C. Gao, D. Dochain, Noise-to-state exponentially stabilizing (state, input)-disturbed CSTRs with non-vanishing noise, Automatica, 142 (2022), 110387. https://doi.org/10.1016/j.automatica.2022.110387 doi: 10.1016/j.automatica.2022.110387
    [15] A. Schaum, S. Tronci, R. Baratti, J. Alvarez, On the dynamics and robustness of the chemostat with multiplicative noise, IFAC, 54 (2021), 342–347. https://doi.org/10.1016/j.ifacol.2021.08.265 doi: 10.1016/j.ifacol.2021.08.265
    [16] S. B. Hsu, T. K. Luo, P. Waltman, Competition between plasmid-bearing and plasmid-free organisms in a chemostat with an inhibitor, J. Math. Biol., 34 (1995), 225–238. https://doi.org/10.1007/BF00178774 doi: 10.1007/BF00178774
    [17] G. S. K. Wolkowicz, H. Xia, S. Ruan, Competition in the chemostat: A distributed delay model and its global asymptotic behavior, SIAM J. Appl. Math., 57 (1997), 1281–1310. https://doi.org/10.1137/S0036139995289842 doi: 10.1137/S0036139995289842
    [18] P. D. Leenheer, B. Li, H. L. Smith, Competition in the chemostat: Some remarks, Can. Appl. Math. Quart., 11 (2003), 229–248.
    [19] S. Yuan, T. Zhang, Dynamics of a plasmid chemostat model with periodic nutrient input and delayed nutrient recycling, Nonlinear Anal.-Real., 13 (2012), 2104–2119. https://doi.org/10.1016/j.nonrwa.2012.01.006 doi: 10.1016/j.nonrwa.2012.01.006
    [20] T. Bayen, J Harmand, M. Sebbah, Time-optimal control of concentrations changes in the chemostat with one single species, Appl. Math. Model., 50 (2017), 257–278. https://doi.org/10.1016/j.apm.2017.05.037 doi: 10.1016/j.apm.2017.05.037
    [21] T. Mtar, R. Fekih-Salem, T. Sari, Interspecific density-dependent model of predator-prey relationship in the chemostat, Int. J. Biomath., 14 (2021), 1–22. https://doi.org/10.1142/S1793524520500862 doi: 10.1142/S1793524520500862
    [22] G. Stephanopoulos, R. Aris, A. G. Fredrickson, A stochastic analysis of the growth of competing microbial populations in a continuous biochemical reactor, Math. Biosci., 45 (1979), 99–135. https://doi.org/10.1016/0025-5564(79)90098-1 doi: 10.1016/0025-5564(79)90098-1
    [23] C. Xu, S. Yuan, T. Zhang, Asymptotic behavior of a chemostat model with stochastic perturbation on the dilution rate, Abstr. Appl. Anal., 2013 (2013), 423154. https://doi.org/10.1155/2013/423154 doi: 10.1155/2013/423154
    [24] D. Zhao, S. Yuan, Critical result on the break-even concentration in a single-species stochastic chemostat model, J. Math. Anal. Appl., 434 (2016), 1336–1345. https://doi.org/10.1016/j.jmaa.2015.09.070 doi: 10.1016/j.jmaa.2015.09.070
    [25] L. Wang, D. Jiang, Asymptotic properties of a stochastic chemostat including species death rate, Math. Meth. Appl. Sci., 41 (2018), 438–456. https://doi.org/10.1002/mma.4624 doi: 10.1002/mma.4624
    [26] L. Imhof, S. Walcher, Exclusion and persistence in deterministic and stochastic chemostat models, J. Differ. Equations, 217 (2005), 26–53. https://doi.org/10.1016/j.jde.2005.06.017 doi: 10.1016/j.jde.2005.06.017
    [27] D. Zhao, S. Yuan, Sharp conditions for the existence of a stationary distribution in one classical stochastic chemostat, Appl. Math. Comput., 339 (2018), 199–205. https://doi.org/10.1016/j.amc.2018.07.020 doi: 10.1016/j.amc.2018.07.020
    [28] C. Xu, S. Yuan, Competition in the chemostat: A stochastic multi-species model and its asymptotic behavior, Math. Biosci., 280 (2016), 1–9. https://doi.org/10.1016/j.mbs.2016.07.008 doi: 10.1016/j.mbs.2016.07.008
    [29] C. Xu, S. Yuan, T. Zhang, Competitive exclusion in a general multi-species chemostat model with stochastic perturbations, Bull. Math. Biol., 83 (2021), 4. https://doi.org/10.1007/s11538-020-00843-7 doi: 10.1007/s11538-020-00843-7
    [30] C. Xu, S. Yuan, T. Zhang, Average break-even concentration in a simple chemostat model with telegraph noise, Nonlinear Anal.-Hybri., 29 (2018), 373–382. https://doi.org/10.1016/j.nahs.2018.03.007 doi: 10.1016/j.nahs.2018.03.007
    [31] M. Gao, D. Jiang, Ergodic stationary distribution of a stochastic chemostat model with regime switching, Phys. A, 524 (2019), 491–502. https://doi.org/10.1016/j.physa.2019.04.070 doi: 10.1016/j.physa.2019.04.070
    [32] T. Caraballo, M. J. Garrido-Atienza, J. López-de-la-Cruz, A. Rapaport, Modeling and analysis of random and stochastic input flows in the chemostat model, Discrete Cont. Dyn.-B, 24 (2019), 3591–3614. https://doi.org/10.3934/dcdsb.2018280 doi: 10.3934/dcdsb.2018280
    [33] X. Zhang, R. Yuan, Pullback attractor for random chemostat model driven by colored noise, Appl. Math. Lett., 112 (2021), 106833. https://doi.org/10.1016/j.aml.2020.106833 doi: 10.1016/j.aml.2020.106833
    [34] M. Gao, D. Jiang, T. Hayat, The threshold of a chemostat model with single-species growth on two nutrients under telegraph noise, Commu. Nonlinear Sci., 75 (2019), 160–173. https://doi.org/10.1016/j.cnsns.2019.03.027 doi: 10.1016/j.cnsns.2019.03.027
    [35] Z, Cao, X. Wen, H. Su, L. Liu, Stationary distribution of a stochastic chemostat model with Beddington-DeAngelis functional response, Phys. A, 554 (2020), 124634. https://doi.org/10.1016/j.physa.2020.124634 doi: 10.1016/j.physa.2020.124634
    [36] X. Zhang, R. Yuan, Sufficient and necessary conditions for stochastic near-optimal controls: A stochastic chemostat model with non-zero cost inhibiting, Appl. Math. Model., 78 (2020), 601–626. https://doi.org/10.1016/j.apm.2019.10.013 doi: 10.1016/j.apm.2019.10.013
    [37] R. Durrett. Stochastic calculus, Boston: CRC Press, 1996.
    [38] N. Ikeda, S. Watanabe, A comparison theorem for solutions of stochastic differential equations and its applications, Osaka J. Math., 14 (1977), 619–633.
    [39] J. Grasman, Stochastic epidemics: The expected duration of the endemic period in higher dimensional models, Math. Biosci., 152 (1998), 13–27. https://doi.org/10.1016/S0025-5564(98)10020-2 doi: 10.1016/S0025-5564(98)10020-2
    [40] L. Arnold, Random dynamical system, New York: Springer, 1998.
    [41] G. Cai, Y. Lin, Probabilistic structural synamics: Advanced theory and applications, New York: McGraw-Hill, 2004.
    [42] C. Xu, S. Yuan, An analogue of break-even concentration in an simple stochastic chemostat model, Appl. Math. Lett., 48 (2015), 62–68. https://doi.org/10.1016/j.aml.2015.03.012 doi: 10.1016/j.aml.2015.03.012
    [43] Q. Liu, Q. Chen, Density function analysis for a stochastic SEIS epidemic model with non-degenerate diffusion, Discrete Cont. Dyn.-B, 26 (2021), 4359–4373. https://doi.org/10.3934/dcdsb.2020291 doi: 10.3934/dcdsb.2020291
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